Minimum Data Set Research Articles

Sacral neuromodulation in nursing home residents: Predictors of success and complications in a national cohort of older adults.

There is limited evidence to support the efficacy of sacral neuromodulation (SNM) for older adults with overactive bladder (OAB). This study aims to report outcomes following SNM among nursing home (NH) residents, a vulnerable population with high rates of frailty and comorbidity. This is a retrospective cohort study of long-stay NH residents who underwent a trial of percutaneous nerve evaluation (PNE) or Stage 1 permanent lead placement (Stage 1) between 2014 and 2016. Residents were identified using the Minimum Data Set linked to Medicare claims. The primary outcome of this study was successful progression from trial to implant. Rates of 1-year device explant/revisions were also investigated. Trial of SNM was observed in 1089 residents (mean age: 77.9 years). PNE was performed in 66.9% of residents and 33.2% underwent Stage 1. Of Stage 1 procedures, 23.8% were performed with simultaneous device implant (single-stage). Overall, 53.1% of PNEs and 72.4% of Stage 1 progressed to device implant, which was associated with Stage 1 procedure versus PNE (adjusted relative risk [aRR]: 1.34; 95% confidence interval [95% CI]: 1.21-1.49) and female versus male sex (aRR: 1.26; 95% CI: 1.09-1.46). One-year explant/revision was observed in 9.3% of residents (6.3% for PNE, 10.5% for Stage 1, 20.3% single-stage). Single stage procedure versus PNE was significantly associated with device explant/revision (aRR: 3.4; 95% CI: 1.9-6.2). In this large cohort of NH residents, outcomes following SNM were similar to previous reports of younger healthier cohorts. Surgeons managing older patients with OAB should use caution when selecting patients for single stage SNM procedures.

Adolescent Idiopathic Scoliosis Minimum Data Set: Towards Standardization of Data Elements in History and Physical Examination

Adolescent Idiopathic Scoliosis Minimum Data Set: Towards Standardization of Data Elements in History and Physical Examination

Combining Soil Immobilization and Dressing Techniques for Sustaining the Health of Metal-Contaminated Arable Soils

The combination of lime immobilization of metals and soil dressing has been a prevalent practice in Korea for remediating metal-contaminated arable soils. However, there have been limited reports on whether this method effectively sustains soil health after remediation, particularly in arable soils. This study undertook a comparative assessment of the soil health index (SHI) across metal-contaminated arable lands, arable soils remediated with lime immobilization and soil dressing, and uncontaminated soils. A total 389 soil samples were collected from these sites and analyzed for nineteen indicators encompassing physical, chemical, and biological properties. To assess soil health, these indicators were screened using principal component analysis, yielding five minimum data set (MDS) indicators: total nitrogen, clay content, dehydrogenase activity, bacterial colony-forming units, and available phosphorus. Among these MDS indicators, total nitrogen exhibited the highest value as the principal component contributing to soil health assessment. Scores of the MDS indicators exhibited significant correlation with those of total data set indicators, affirming the appropriateness of the soil health assessment adopted in this study. The SHI of the remediated arable soils (0.48) surpassed those of the contaminated soils (0.47) and were statistically comparable to those of the uncontaminated forest (0.51) and upland (0.51) soils. The health of the contaminated soils demonstrated a high dependence on soil properties rather than metal concentrations. These findings underscore the robustness of the combined immobilization and soil dressing method for sustaining the health of contaminated arable soils post-remediation.

Upper gastrointestinal and hepatopancreaticobiliary surgery in New Zealand: Balancing the volume-outcome relationship with accessibility in a surgically low volume country.

New Zealand has a population of only 5.5 million meaning that for many surgical procedures the country qualifies as a "low-volume center." However, the health system is well developed and required to provide complex surgical procedures that benchmark internationally against comparable countries. This investigation was undertaken to review regional variation and volumes of complex resection and palliative upper gastrointestinal (UGI) surgical procedures within New Zealand. Data pertaining to patients undergoing complex resectional UGI procedures (esophagectomy, gastrectomy, pancreatectomy, and hepatectomies) and palliative UGI procedures (esophageal stenting, enteroenterostomy, biliary enteric anastomosis, and liver ablation) in a New Zealand hospital between January 1, 2000 and December 31, 2019 were obtained from the National Minimum Dataset. New Zealand is a low-volume center for UGI surgery (229 hepatectomies, 250 gastrectomies, 126 pancreatectomies, and 74 esophagectomies annually). Over 80% of patients undergoing hepatic resection/ablation, gastrectomy, esophagectomy, and pancreatectomy are treated in one of the six national cancer centers (Auckland, Waikato, Mid-Central, Capital Coast, Canterbury, or Southern). There is evidence of the decreasing frequency of these procedures in small centers with increasing frequency in large centers suggesting that some regionalization is occurring. Palliative procedures were more widely performed. Indigenous Māori were less likely to be treated in a nationally designated cancer center than non-Māori. The challenge for New Zealand and similarly sized countries is to develop and implement a system that optimizes the skills and pathways that come from a frequent performance of complex surgery while maintaining system resilience and ensuring equitable access for all patients.

Evaluation of Soil Quality and Analysis of Barriers of Protection Forests along Tarim Desert Highway Based on a Minimum Data Set

To gain a thorough grasp of the soil quality conditions in the preservation forests along the Tarim Desert Highway, a detailed assessment of the research area’s soil quality is necessary, along with the identification of any potential obstacles. This evaluation should identify any potential obstacles and provide a theoretical basis for the sustainable utilization and precise management of protection forest soils. This paper examines the protection forest along the Tarim Desert Highway as the study area. To characterize the features of the soil quality, thirteen indicators of the chemical, physical, and biological composition of the soil were examined. The principal component analysis method was used to construct the minimum dataset (MDS) for soil quality evaluation. The diagnostic model for obstacle factors was combined with the MDS to explore the soil quality characteristics and obstacle factors in the study region. The findings indicated that (1) the constructed indexes of the MDS of protection forest soil in the study area included soil ammonium nitrogen (NH4+-N); quick-acting phosphorus (AvP); organic carbon (SOC); alkaline phosphatase (AP); and total salt (SS); (2) soil quality based on the MDS and the total dataset (TDS) showed a significant positive correlation (R2 = 0.748; p < 0.05), and the research region’s soil quality were all in the medium and below level (100% of SQI ≤ 0.6), showing the status quo of high soil quality in the two ends of the shelterbelt forest and the tower center area, while the other areas were relatively low; (3) according to the findings of the soil barrier analysis, the two main obstacles influencing the soil quality of the shelterbelt forest at this time are NH4+-N and SOC, and that the soil quality of the shelterbelt forest could be improved by artificially increasing the inputs of nutrients and precise conservation measures, promoting the overall function of artificial protective forests on desert highways. In addition to providing a solid scientific foundation for the sustainable use and maintenance of shelterbelt forests along the Tarim Desert Highway, this study is also an invaluable resource for researching the soil quality of artificial shelterbelt forests in arid regions.

A framework for selecting and assessing soil quality indicators for sustainable soil management in waste dumps

The primary objective of this study was to develop soil quality indexes (SQIs) to reveal the changes in SQ during the restoration of vegetation in the reclaimed waste dumps of the Hequ open-pit coal mine. The study built an SQI evaluation model for waste dumps based on the soil management assessment framework. The total data set (TDS) consisted of nine physicochemical property indicators. The selection of the minimum data set (MDS) involved the utilization of principal component analysis (PCA) and Norm values. The SQ was comprehensively evaluated for nine indicators, taking into account the non-linear membership function and the improved Nemerow index. The findings suggested a notable disparity in the SQ between the reclaimed area and the unreclaimed area, yet the overall SQ fell short. In the TDS index system, the organic matter has the highest weight and a greater contribution to the soil quality of the waste dumps. In the MDS indicator system, the weights of organic matter and total nitrogen are both 0.5. According to Nemerow index method, the average SQIN of 5 plots is calculated to be 0.4352 ± 0.194. The average value obtained from TDS is 0.581 ± 0.236, and the average value obtained from MDS is 0.602 ± 0.351. The weighted additive method was employed to compute three SQIs, all of which yielded satisfactory outcomes. And the above evaluation methods indicate that the overall soil quality level of the waste dumps is at a moderate level. The sequence of SQ in various waste dumps was as follows: No.4lower > No.1 > No.2 > No.3 > No.4upper. Specifically, the non-linear membership function indicated that pH, available nitrogen (AN), available phosphorus (AP), surface moisture content (SMC), and bulk density (BD) were crucial in limiting SQIs in total waste dumps. The crucial limiting SQIs in unreclaimed areas were total phosphorus (TP) and total nitrogen (TN). This analysis demonstrates its efficacy in formulating strategies for the SQ evaluation and targeted soil reclamation plans of waste dumps.

Enhancing cross-subject transfer performance for SSVEP identification using small data-based transferability evaluation

Cross-subject steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have attracted increasing attention in recent years due to their potential to reduce calibration efforts and improve usability. This study proposed an efficient cross-subject transfer method for SSVEP-BCI, which significantly reduced the calibration effort and enhanced system performance. By employing a minimal dataset obtained from the target subject, specifically, a single trial for each frequency, we calculated the similarity between the SSVEPs of the target subject and the source subjects. Based on the similarities, a transferability evaluation model was constructed, which was then used to assign weights to the source subjects. The features calculated from the source subjects were then weighted and summed to obtain the frequency recognition results. The proposed cross-subject transfer method was evaluated in two pipelines based on the ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA), respectively. The proposed method in eTRCA-pipeline initially achieving accuracies of 96.98 ± 3.66 % and 88.97 ± 14.66 % in our dataset and the Benchmark dataset, respectively, at a data length of 1 s. After fusing the features of eTRCA with those of the extended canonical correlation analysis (eCCA), the method exhibited enhanced performance, elevating accuracies to 98.23 ± 2.68 % and 92.83 ± 10.75 %. Similarly, in TDCA-pipeline, accuracies initially reached 97.47 ± 3.88 % and 93.53 ± 10.22 % in our dataset and the Benchmark dataset, respectively, and after fusing TDCA features with eCCA features, they remarkably increased to 98.13 ± 2.89 % and 95.00 ± 8.43 %, respectively. This study offers a promising solution for the fast and effective calibration of SSVEP-BCI, paving the way for more practical and accessible BCI systems.

How to measure patient and family important outcomes in extremely preterm infants: A scoping review.

Parents of children born preterm have identified outcomes to be measured for audit and research at 18-24 months of age: child well-being, quality of life/function, socio-emotional/behavioural outcomes, respiratory, feeding, sleeping, and caregiver mental health. The aim was to identify the best tools to measure these seven domains. Seven working groups completed literature reviews and evaluated potential tools to measure these outcomes in children aged 18-24 months. A group of experts and parents voted on the preferred tools in a workshop and by questionnaire. Consensus was 80% agreement. Consensus was obtained for seven brief, inexpensive, parent friendly valid measures available in English or French for use in a minimum dataset and potential alternative measures for use in funded research. Valid questionnaires and tools to measure parent-identified outcomes in young preterm children exist. This study will facilitate research and collection of data important to families.

From Sensors to Insights: An Original Method for Consumer Behavior Identification in Appliance Usage

In light of the energy crisis, extensive research is being conducted to enhance load forecasting, optimize the targeting of demand response programs, and advise building occupants on actions to enhance energy performance. Cluster analysis is increasingly applied to usage data across all consumer types. More accurate consumer identification translates to improved resource planning. In the context of Industry 4.0, where comprehensive data are collected across various domains, we propose using existing sensor data from household appliances to extract the usage patterns and characterize the resource demands of consumers from residential households. We propose a general pipeline for extracting features from raw sensor data alongside global features for clustering device usages and classifying them based on extracted time series. We applied the proposed method to real data from three different types of household devices. We propose a strategy to identify the number of existent clusters in real data. We employed the label data obtained from clustering for the classification of consumers based on data recorded on different time ranges and achieved an increase in accuracy of up to 15% when we expanded the time range for the recorded data on the entire dataset, obtaining an accuracy of over 99.89%. We further explore the data meta-features for a minimal dataset by examining the necessary time interval for the recorded data, dataset dimensions, and the feature set. This analysis aims to achieve an effective trade-off between time and performance.

Multiple Soil Health Assessment Methods for Evaluating Effects of Organic Fertilization in Farmland Soil of Agro-Pastoral Ecotone

The incorporation of organic fertilizer is an important practice to improve the sustainability and productivity of crop production and decrease environmental pollution from crop-livestock systems in global agriculture. However, establishing an evaluation dataset is still the main challenge for quickly and effectively assessing the effect of management measures on farmland soil health. Hereby, we developed a minimum dataset (MDS) using three methods (network analysis (NA), random forest analysis (RF), and principal component analysis (PCA)). Based on MDS and two scoring functions (nonlinear (NL) and linear scoring curve (L)), the SHI (soil health index framework) was constructed to assess soil health conditions under four fertilization treatments (no fertilization, CK; only chemical fertilizer, NPK; only cow manure, MF; 50% chemical fertilizer + 50% cow manure, NPKM) in the northern ecotone of China. The results showed that the MDS-based on SHIs were positively correlated with each other and SHI-TDS (total dataset), verifying the consistency of soil health assessment models. Higher R2 was observed in the fitting of SHIs based on NA and TDS, which suggested that nMDS (minimum dataset based on network analysis) could represent most of the information in the TDS. The SHI-NL-nMDS (based on network analysis and nonlinear scoring curve) has the highest ability of sensitivity and accuracy, which indicates that compared with PCA and RF, the SHI based on NA can better embody farmland sol ecosystem functions. In addition, crop yield was significantly positive relative to SHI (soil health index). The incorporation of cow manure and chemical fertilizer improved soil health and increased crop yield. These results indicate that network analysis was a reliable technology for determining the minimum dataset in the evaluation of farmland soil health, and incorporating livestock manure could improve soil health and crop yield in this study area.

Serious Mental Illness in Assisted Living Communities: Association With Nursing Home Placement

ObjectivesAssess prevalence of serious mental illness (SMI) alone, and co-occurring with Alzheimer disease and related dementias (ADRD), among Medicare beneficiaries in assisted living (AL). Examine the association between permanent nursing home (NH) placement and SMI, among residents with and without ADRD. Design2018-2019 retrospective cohort of Medicare beneficiaries in AL. Residents were followed for up to 2 years to track their NH placement. We used data from the Medicare Enrollment Database, the Medicare Beneficiary Summary File, Minimum Data Set, and a national directory of state-licensed AL communities. AL residents were identified using a validated, previously reported 9-digit zip code methodology. Setting and ParticipantsA cross-sectional study sample included 289,350 Medicare beneficiaries in 17,265 AL communities across 50 states and in the District of Columbia. MethodsThe outcome was permanent NH placement: a continuous stay for more than 90 days. Key independent variable was presence of SMI—schizophrenia, bipolar disorder, and major depression. Other covariates included sociodemographic factors and presence of other chronic conditions, including ADRD. A linear probability model with robust SEs, and AL-level random effects, was used to test the association between SMI diagnoses, ADRD, and their interactions on NH placement. ResultsMore than half (55.65%) of AL residents had a diagnosis of SMI, among them 93.2% had major depression, 28.5% schizophrenia, and 22.2% bipolar disorder. Individuals with schizophrenia and bipolar disorder had a significantly lower probability of NH placement, a 32% and a 15% decrease relative to the cohort mean, respectively. Placement risk was significantly greater for residents with ADRD compared to those without, increasing for those who also had schizophrenia or bipolar disorder, 12.9% and 1.5% relative to the sample mean, respectively. Conclusion and ImplicationsPresence of schizophrenia and bipolar disorder, in conjunction with ADRD, significantly increases the risk of long-term NH placement, suggesting that ALs may not be well prepared to care for these residents.

Soil quality assessment of oak forests invaded by moso bamboo (Phyllostachys edulis) in the northern subtropics

Soil quality is closely related to the evolution of forest ecosystems, especially in the context of global warming; therefore, it is essential to study the changes in soil quality caused by the invasion of moso bamboo in its habitat. In this study, we selected plantation forests and natural secondary forests with typical vegetation of the northern subtropics, Quercus acutissima plantation (QAP) and oak secondary forest (OSF), collected plant community and soil physicochemical data, and established a soil quality index (SQI) to assess how moso bamboo invasion affects soil quality. In addition, the mechanisms driving the moso bamboo invasion effects on soil quality were investigated using a partial least squares structural equation model (PLS-SEM). The results demonstrated that: (1) Moso bamboo invasion significantly affected community structure and soil physicochemical properties, there were inconsistent changes in soil physicochemical indicators in oak forests of different origins, and in general the effects of moso bamboo expansion on soil physicochemical properties were concentrated in the topsoil (0–20cm). (2) Soil bulk density (BD), total soil porosity (TSP), total nitrogen (TN), and available potassium (AK) comprised the minimum data set (MDS) used to calculate the SQI, and the SQI of oak forests of different origins ranged from 0.48 to 1, and the invasion of moso bamboo led to a significant decrease in the SQI of the surface layer of QAP. A redundancy analysis (RDA) showed that soil quality assessment factors were closely related to community structure and the PLS-SEM indicated that moso bamboo invasion indirectly affected surface soil quality through its effects on community and soil factors. The results showed that moso bamboo invasion had different effects on oak forests of different origins. They also showed that soil quality was strongly affected by aboveground biomass (AGB), the tree Shannon-Wiener index (TH'), and available potassium (AK).

Image dermoscopy skin lesion classification using deep learning method: systematic literature review

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.

Potassium and Magnesium in American Ginseng Roots as Key Factors in Monitoring Soil Quality, Yield, and Quality: Screening, Prediction, and Validation

Understanding the key roles of nutrient elements in soil–plant systems are essential for herbal medicine production and sustainable development. However, the ecological relationships between soil quality and nutrient elements, yield, saponins, or other active compounds in American ginseng remain unclear. In this study, 20 soil indicators, 10 root nutrient indicators, 9 quality indicators, and yields were investigated. The minimum dataset was constructed by principal component analysis, key factors were screened by correlation analysis and PLS-PM analysis, and the prediction model was constructed using linear fitting and tested by a validation test. The minimum dataset, constructed based on principal component analysis, comprised five indicators: SOM, TP, AK, AMg, and ACa. Correlation analysis, PLS-PM analysis, and linear fitting showed that K and Mg were the key factors relating soil quality to the yield and quality of American ginseng and that when AMg was 0.21 g/kg and AK was 0.30 g/kg, soil organic matter was 27%, total phosphorus was 1.19 g/kg in soil, K content in roots was 15.63 g/kg, Mg content was 1.91 g/kg, and the K/Mg of 8.85 could balance American ginseng yield and quality. In predicting and validating the model, predicting the DW, total ginsenoside, Rb1, Rb2, Rc, and Rd of American ginseng using K/Mg were reliable. This study provides a scientific basis for nutrient regulation, selecting planting sites, assessing soil quality, and predicting and evaluating American ginseng quality.

Post-acute care transitions during COVID-19: Racial, ethnic, and socioeconomic differences in older adults with Alzheimer's disease and related dementia.

Differences in the post-acute care (PAC) destinations among racial, ethnic, and socioeconomic groups have been documented before the COVID-19 pandemic. Yet, the pandemic's impact on these differences remains unknown. We examined the impact of the COVID-19 pandemic on PAC destinations and its variation by individual race, ethnicity, and socioeconomic status among community-dwelling older adults with Alzheimer's disease and related dementia (ADRD). We linked 2019-2021 national data (Medicare claims, Minimum Data Set, Master Beneficiary Summary File) and several publicly available datasets, including Provider of Services File, Area Deprivation Index, Area Health Resource File, and COVID-19 infection data. PAC discharge destinations included skilled nursing facilities (SNFs), home health agencies (HHA), and homes without services. Key variables of interest included individual race, ethnicity, and Medicare-Medicaid dual status. The analytic cohort included 830,656 community-dwelling Medicare fee-for-service beneficiaries with ADRD who were hospitalized between 2019 and 2021. Regression models with hospital random effects and state-fixed effects were estimated, stratified by the time periods, and adjusted for the individual, hospital, and county-level covariates. SNF discharges decreased while home and HHA discharges increased during the pandemic. The trend was more prominent among racial and ethnic minoritized groups and even more so among dual-eligible beneficiaries. For instance, the reduction in the probabilities of SNF admissions between the pre-pandemic period and the 2nd year of COVID was 4.6 (White non-duals), 18.5 (White duals), 8.7 (Black non-duals), and 20.1 (Black duals) percentage-point, respectively. We also found that non-duals were more likely to replace SNF with HHA services, while duals were more likely to be discharged home without HHA. The COVID-19 pandemic significantly impacted PAC destinations for individuals with ADRD, especially among socioeconomically disadvantaged and racial and ethnic minoritized populations. Future research is needed to understand if and how these transitions may have affected health outcomes.

The short- and long-term effect of osteopathic manipulative treatment on pain, and psychosocial factors in adults with chronic low back pain.

Chronic low back pain (CLBP) has long plagued mankind, but little progress has been made in finding a rational and effective treatment, letalone a common cause. This study is an attempt to fill that void by measuring short- and long-term effects of osteopathic manipulative treatment (OMT), including psychosocial and pain reduction in CLBP patients. The objectives of this study were to investigate the effectiveness of neuromusculoskeletal medicine/osteopathic manipulative medicine (OMM) in treating CLBP, with a focus on biopsychosocial (pain sensitivity questionnaire [PSQ]) and pain control in chronic conditions. The study involved a large, single cohort observational design of 101 patients. The inclusion criteria for selecting patients targeted those with "nonspecific" CLBP. The National Institutes of Health (NIH) Minimum Dataset for Chronic Low Back Pain (NMD) was the measurement tool and was administered at consent (baseline), 2, 4, and 8weeks and at 6 and 12months. Time trends were analyzed as overall mean. Pairwise differences were compared between time points. Mixed-effects models were utilized to test the association of time with pain and biopsychosocial scores. Pain and PSQ scores decreased over the study timeline. The most significant change for both pain and biopsychosocial scores occurred at 6months compared to baseline, with a further reduction at 12months. OMT has been demonstrated to significantly reduce pain and psychosocial factors related to CLBP in both the short and long term.

Functional roles of biocrusts in enhancing soil quality in a semi-arid environment

Biocrusts play a wide range of ecological roles, especially in modifying surface soil properties and as a natural agent to combat soil degradation in fragile semi-arid and arid ecosystems globally. We examined the importance of biocrust on soil properties and quality in a semi-arid alluvial fan in northeastern Iran. The soil quality index (SQI) was used as the method for the quantitative evaluation of soil with or without biocrusts, using standard linear and nonlinear scoring methods. For this purpose, 17 biological, chemical, and physical soil properties were integrated into two indicator selection methods (total and minimum data set) and SQI has been calculated by the weighted additive method. The spatial patterns of SQI showed that overall the soil quality was (47%) increased in biocrust compared to non-biocrust soil. The increase of microbial biomass nitrogen, basal soil respiration, and microbial biomass carbon, and the decrease of available sodium and percentage sand were the most important indicators controlling the soil quality changes by biocrusts. In addition, biocrusts improved the soil structure by reducing bulk density and increasing the mean weight diameter of aggregates and available water capacity. Cyanobacteria communities of biocrusts were the initiators of soil quality improvement, while moss and cyanolichen communities played a major role from the moderate stages of biocrust occurrence. It is concluded that globally, in dry lands, biocrusts should be considered as signatures of enhanced soil quality.

Identification of soil properties influencing primary productivity of fish ponds under red and lateritic soil zones

Aim: The present study aimed to identify the key soil factors influencing the primary productivity of water in the fish ponds situated under low productive red and lateritic soil zones. Methodology: General properties of fish pond soils (n=42) from different red and lateritic soil zones of West Bengal, India were determined along with the gross primary productivity (GPP) values of corresponding pond waters. All the soil properties were correlated with GPP values and the soil factors which were significantly correlated with GPP of pond water were further used for step down regression analysis with regard to GPP.This helped to develop a minimum data set of the key soil factors which showed maximum influence on GPP. The efficiency of the regression equation, generated with the critical soil parameters, in predicting the GPP values of other ponds was also assessed through a study on the variations in calculated and on-farm GPP values of eight fish ponds. Results: Among all the studied soil properties, pH and availability of three major nutrients viz. N, P and K were observed to contribute 84.15% variations in gross production of primary fish food organisms in these pond environment. Of these 4 soil properties, again, pH and available P values of the pond soils appeared to be more important. A regression equation was also developed to predict the GPP of such pond water using soil pH and available P values only. This equation showed a precision range of 83.0 to 99.7% and was found to be statistically at par with the GPP values observed under actual pond condition. Interpretation: High efficiency in predictability of pond GPP values using the minimum data set of two key soil factors viz. soil pH and available P indicates that proper management of these productivity attributing soil properties will be particularly helpful to improve the primary productivity levels of the predominantly low yielding fish ponds under the red and lateritic soil zones. Key words: Fish ponds, Minimum data set, Productivity attributes, Red and lateritic soil zones, Soil factors

Abstract 2249: Clinical outcomes of discontinuing long-term opioid therapy among older cancer survivors in nursing homes

Abstract Purpose: The purpose of this study is to investigate associations of discontinuing vs continuing long-term opioid therapy (LTOT) with pain, physical function, and depressive symptoms among older cancer survivors who resided in nursing homes. Pain is prevalent and use of LTOT is common among patients with cancer, including those living in nursing homes. The Centers for Disease Control and Prevention (CDC) Guideline for Prescribing Opioids for Chronic Pain—United States recommends tapering or discontinuing LTOT for patients with chronic pain when risks outweigh the benefits of LTOT. Despite the guideline explicitly stating that its recommendation does not apply to patients with cancer, doctors including oncologists have been prescribing fewer opioid medications for older patients, raising a question regarding the benefit and risk of discontinuing LTOT for cancer survivors who depend on opioids for pain relief. Study Design: We conducted a cohort study using a 100% Medicare nursing home sample linked to the Minimum Data Set (MDS) from 2010 to 2021. Participants included older (≥65 years of age) long-term (≥2 years) residents with a diagnosis of cancer who received LTOT for ≥90 days in a 12-month nursing home stay. Residents entered the cohort on the 90th day of the latest LTOT episode. Residents who received palliative or hospice care were excluded. The key exposure was discontinuation of LTOT, defined as no opioid refills for ≥90 consecutive days in 1 year after cohort entry. The primary outcomes were pain (measured using a numerical rating scale or categorical verbal descriptor scale), physical function (9-item activities of daily living scale), and depression (Patient Health Questionnaire-9) from baseline to quarterly assessments during 1-year follow-up after the discontinuation of LTOT. Results: In total, 53,141 cancer survivors who received LTOT were identified (mean [SD] age, 82.9 [8.7] years; 39,522 [74.4%] female; 46678 [87.8] Whites), consisting of 62,387 patient observations during the 10-year study period. Of 62,387 patient observations, 51,357 residents (82.3%) continued LTOT, and 11,030 (17.6%) discontinued LTOT. Residents who discontinued (vs continued) LTOT had lower odds of worsening pain (adjusted odds ratio [AOR], 0.64, 95% CI, 0.59-0.59, P < .001) and depressive symptoms (AOR, 0.92 [95% CI, 0.85-0.99]; P = .033), with no difference in physical function (AOR, 0.96 [95% CI, 0.89-1.03]; P = .226) during the 1-year follow-up. Conclusion: Among older nursing home residents with cancer, discontinuing vs continuing LTOT was associated with lower odds of worsening pain and depressive symptoms. Funding source: NIH/NIA R01AG073442. Citation Format: Yu-Jung Jenny Wei, Almut G. Winterstein, Siegfried Schmidt, Roger B. Fillingim, Michael Daniels, Steven T. DeKosky, Ben Kinder, Ting-Yuan David Cheng. Clinical outcomes of discontinuing long-term opioid therapy among older cancer survivors in nursing homes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2249.

Using a soil quality index to evaluate the artificial soil quality response to rehabilitation time

External-soil spray seeding (ESSS) supplies nutrients and water to plants, soils play a pivotal role in the successful rehabilitation of rock-cut slopes. However, few studies have explored how to accurately evaluate the artificial soil quality response to rehabilitation time to determine the effectiveness of the ESSS technique. In this study, taking the rock-cut slopes of Suining Railway Station to study, various soil samples were obtained from 20-year-old, 13-year-old and 9-year-old rehabilitation slopes, where we sampled the surface soil in north- and south-facing slopes, respectively. Fifteen soil indicators were measured as potential indicators of artificial soil quality, and the indicators with significant differences between rehabilitation time considered as a total data set (TDS). Principal component analysis (PCA) and correlation matrix analysis (CMA) were used to determine a minimum data set (MDS), and these indicators within MDS were integrated into a soil quality index (SQI) by standardized scoring functions (SSF) and weighted-additive methods. Results suggested that (1) the SQI was an effective tool for evaluating artificial soil quality, (2) the longer rehabilitation time, the higher artificial soil quality, and (3) the SQI of the north-facing slopes was higher than that of the south-facing slopes in the same rehabilitation years. Nevertheless, all rehabilitation slopes had a low SQI (SQI ≤ 0.560). Overall, ESSS is a technology that can effectively promote the ecological restoration of rock-cut slopes, but there still exist a room for improvement. In the future ecological reconstruction of rock-cut slopes, more attention should be paid to the restoration of soil quality on the south-facing slopes.