Public Settings Research Articles

Real-Time Face Mask Detection Using MobileNet V2

Abstract: The rapid disruption of international trade and travel caused by the COVID-19 epidemic has had an impact on our daily lives. The custom of donning a face mask for protection has evolved. In the near future, a lot of public service providers will demand that clients wear appropriate masks in order to use their services. Identification of face masks is becoming a crucial duty to support world culture. This paper outlines a condensed method for accomplishing this goal utilizing certain fundamental machine learning tools, such as TensorFlow, Keras, OpenCV, and Scikit-Learn. The project's goal is to identify face masks at a public event or gathering. MobileNet V2 is the algorithm employed in the project to accomplish the goal. There is a picture of a few people with and without masks. As an input dataset, a picture of a few individuals wearing and not wearing masks is employed. Pre- processing, data augmentation, training, testing, and image segmentation are some of the steps that go into reaching the project's goal. Creating a model that can identify people who are not wearing masks in public settings is the goal of this project. To guarantee adherence to the standards for public safety, this work can be combined with real-time applications at airports, train stations, businesses,schools, and other public locations.

Validity of the 1984 Interim Guidelines on Airborne Ultrasound and Gaps in the Current Knowledge.

Airborne ultrasound is used for various purposes both in industrial and public settings, as well as being produced as a by-product by a range of sources. The International Radiation Protection Association (IRPA) published interim guidelines on limiting human exposure to airborne ultrasound in 1984, based on the limited scientific evidence that was available at that time. In order to investigate whether research since 1984 requires the development of revised exposure guidelines we considered (a) within the context of ultrasound exposure the relevance to health of the biological endpoints/mechanisms listed in the IRPA guidelines, (b) the validity of the exposure limits, and (c) whether there are biological endpoints/mechanisms not covered in the guidelines. The analysis of the available evidence showed that the biological endpoints that form the basis of the guidelines are relevant to health and the guidelines provide limits of exposure based on the evidence that was available at the time. However, the IRPA limits and their associated dosimetry were based on limited evidence, which may not be considered as scientifically substantiated. Further, there is no substantiated evidence of biological endpoints/mechanisms not covered by the IRPA guidelines. These two observations could mean that IRPA's limits are too low or too high. Research since the IRPA guidelines has made some improvements in the knowledge base, but there are still significant data gaps that need to be resolved before a formal revision of the guidelines can be made by ICNIRP, including research needs related to health outcomes and improved dosimetry. This statement makes a number of recommendations for future research on airborne ultrasound.

Cost-effectiveness analysis of surgical masks, N95 masks compared to wearing no mask for the prevention of COVID-19 among health care workers: Evidence from the public health care setting in India.

Nonpharmacological interventions, such as personal protective equipment for example, surgical masks and respirators, and maintenance of hand hygiene along with COVID-19 vaccines have been recommended to reduce viral transmission in the community and health care settings. There is evidence from the literature that surgical and N95 masks may reduce the initial degree of exposure to the virus. A limited research that has studied the cost-effective analysis of surgical masks and N95 masks among health care workers in the prevention of COVID-19 in India. The objective of this study was to estimate the cost-effectiveness of N95 and surgical mask compared to wearing no mask in public hospital settings for preventing COVID-19 infection among Health care workers (HCWs) from the health care provider's perspective. A deterministic baseline model, without any mask use, based on Eikenberry et al was used to form the foundation for parameter estimation and to estimate transmission rates among HCWs. Information on mask efficacy, including the overall filtering efficiency of a mask and clinical efficiency, in terms of either inward efficiency(ei) or outward efficiency(e0), was obtained from published literature. Hospitalized HCWs were assumed to be in one of the disease states i.e., mild, moderate, severe, or critical. A total of 10,000 HCWs was considered as representative of the size of a tertiary care institution HCW population. The utility values for the mild, moderate and severe model health states were sourced from the primary data collection on quality-of-life of HCWs COVID-19 survivors. The utility scores for mild, moderate, and severe COVID-19 conditions were 0.88, 0.738 and 0.58, respectively. The cost of treatment for mild sickness (6,500 INR per day), moderate sickness (10,000 INR per day), severe (require ICU facility without ventilation, 15,000 INR per day), and critical (require ICU facility with ventilation per day, 18,000 INR) per day as per government and private COVID-19 treatment costs and capping were considered. One way sensitivity analyses were performed to identify the model inputs which had the largest impact on model results. The use of N95 masks compared to using no mask is cost-saving of $1,454,632 (INR 0.106 billion) per 10,000 HCWs in a year. The use of N95 masks compared to using surgical masks is cost-saving of $63,919 (INR 0.005 billion) per 10,000 HCWs in a year. the use of surgical masks compared to using no mask is cost-saving of $1,390,713 (INR 0.102 billion) per 10,000 HCWs in a year. The uncertainty analysis showed that considering fixed transmission rate (1.7), adoption of mask efficiency as 20%, 50% and 80% reduces the cumulative relative mortality to 41%, 79% and 94% respectively. On considering ei = e0 (99%) for N95 and surgical mask with ei = e0 (90%) the cumulative relative mortality was reduced by 97% and the use of N95 masks compared to using surgical masks is cost-saving of $24,361 (INR 0.002 billion) per 10,000 HCWs in a year. Both considered interventions were dominant compared to no mask based on the model estimates. N95 masks were also dominant compared to surgical masks.

Adopting agile in government: a comparative case study

ABSTRACT This study examines the adoption of agile in public administrations through the lens of Scandinavian institutionalism and translation theory. By conducting interviews in 19 German public administrations, we investigate how agile is translated into public settings and how they address associated challenges. Our findings reveal three translation modes: agile stripped down to a cultural concept, agile as governance to foster cross-functional collaboration, and agile as methodology, adhering closely to its original values and principles. As such, our study contributes to a better understanding of and theoretical advancements surrounding agile in government.

Nurse’s role as a counselor and adherence to antiretroviral therapy among HIV/AIDS patients: a cross-sectional study in a public health center setting in Indonesia

Nurse’s role as a counselor and adherence to antiretroviral therapy among HIV/AIDS patients: a cross-sectional study in a public health center setting in Indonesia

Preceptorship in robotic colorectal surgery: experience from the Australian private sector

This article describes a post-fellowship preceptorship training program to train sub-specialty colorectal surgeons in gaining proficiency in robotic colorectal surgery using a dual-surgeon model in the Australian private sector. The Australian colorectal surgeon faces challenges in gaining robotic colorectal surgery proficiency with limited exposure and experience in the public setting where the majority of general and colorectal surgery training is currently conducted. This training model uses graded exposure with a range of simulation training, wet lab training, and clinical operative cases to progress through both competency and proficiency in robotic colorectal surgery which is mutually beneficial to surgeons and patients alike. Ongoing audit of practice has shown no adverse impacts.

Applying the Participatory Evaluation Measurement Instrument in the Evaluation of a Public School Program for Teachers

Although most evaluators are familiar with participatory evaluation (PE), the ability to measure stakeholder participation in an evaluation remains challenging. Based on Cousins and Whitmore’s (1998) PE theoretical model, Daigneault and Jacob (2009, 2012, 2014) developed an instrument for measuring the degree to which an evaluation can be considered participatory. The present study used Daigneault and Jacob's Participatory Evaluation Measurement Instrument (PEMI) in the evaluation of a public school district's educator mentoring program. The evaluators examined the benefits and challenges of using the instrument in public school settings. This paper details the procedure of using PEMI to explore the level of stakeholder participation in the evaluation case and uses a process similar to the mini-ethnography method to reflect on the experiences of the evaluators through the procedure. Findings show that in addition to accurately capturing the perceived level of participation, PEMI may be utilized in the planning phases of evaluation.

Unveiling school community perceptions of nutrition-friendly school initiatives in preschool settings in rural Sindh province, Pakistan: an exploratory study

IntroductionIn 1995, the World Health Organization launched its Global School Health Initiative to expand the Health Promoting School (HPS). The objective of this study was to explore the perception of the school community in implementing nutrition-friendly school initiatives in preschool settings.MethodsThis paper delineates the exploratory phase of a mixed-method study, which broadly aims to assess the adoption of the Nutrition Friendly School Initiative (NFSI) through a preschool preparedness intervention package in rural Sindh province, Pakistan. The study sites include three campuses of the Aga Khan School (Mirpur Sakro, Junior Campus Vur, and a community-based school in Sujawal). Participants were selected purposively from these campuses and constituted a committee named ‘school community,’ which was responsible for implementing all activities outlined in the intervention package. Data was gathered through in-depth interviews and consultative meeting with the school community. Thematic analysis was employed for data analysis.ResultsThe analysis established five major themes that represent the participants’ perception of school-based nutrition interventions in preschool settings. These five themes include (i) Challenges in health and nutrition for pre-school age children, (ii) Clarity in roles and responsibilities for school-based nutrition intervention, (iii) Advancing school-based nutrition activities and interventions, (iv) Recognizing resources requirements, (v) Opportunities and challenges for the way forward in school-based nutrition. Findings also suggest sustainability and scalability measures that include the aligning School Nutrition Policy with the school handbook, ways to engage with parents, a nutrition theme corner, the inclusion of a parenting component in the nutrition manual, and capacity building of the school community.ConclusionQualitative findings have guided the refinement of the intervention package, proposing additional measures for sustainability and scalability across AKES, P. The school community is hopeful that the implementation of the refined intervention package will enhance preschool preparedness toward achieving nutrition-friendly school checklist items. This study holds strong potential for replication in a public school setting and presents an opportunity to launch a school accreditation program to certify schools as Nutrition-friendly.

Optimizing deep learning-based segmentation of densely packed cells using cell surface markers

BackgroundSpatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge.MethodsWe first assess the performance of 18 deep learning-based cell segmentation models, either pre-trained or trained by us using two public image sets, on a set of immunofluorescence images stained with immune cell surface markers in skin tissue obtained during human herpes simplex virus (HSV) infection. We then further train eight of these models using up to 10,000+ training instances from the current image set. Finally, we seek to improve performance by tuning parameters of the most successful method from the previous step.ResultsThe best model before fine-tuning achieves a mean Average Precision (mAP) of 0.516. Prediction performance improves substantially after training. The best model is the cyto model from Cellpose. After training, it achieves an mAP of 0.694; with further parameter tuning, the mAP reaches 0.711.ConclusionSelecting the best model among the existing approaches and further training the model with images of interest produce the most gain in prediction performance. The performance of the resulting model compares favorably to human performance. The imperfection of the final model performance can be attributed to the moderate signal-to-noise ratio in the imageset.

Cost-effectiveness of mask mandates on subways to prevent SARS-CoV-2 transmission in the United States.

Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel. We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022). Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership). The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases.

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today’s culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. This review highlights the substantial influence of social media in advancing public health objectives. The key themes explored encompass the utilization of social media to advance health initiatives, monitor diseases, track behaviors, and interact with communities. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings show how social media has been used as a tool for research, implementing health campaigns, and health promotion. Social media integration with artificial intelligence (AI) and Generative Pre-Trained Transformers (GPTs) can impact and offer an innovative approach to tackle the problems and difficulties in health informatics. The research shows how social media will keep growing and evolving and, if used effectively, has the potential to help close public health gaps across different cultures and improve population health.

Contrasting YOLOv7, SSD, and DETR on Insulator Identification under Small-sample Learning

Background:: Daily inspections of insulators are necessary because they are indispensable components for power transmission lines. Using deep learning to monitor insulators is a newly developed method. However, most deep learning-based detection methods rely on a large training sample set, which consumes computing resources and increases the workload of sample labeling. The selection of learning models to monitor insulators becomes problematic. Objective:: Through comparative analysis, a model suitable for small-sample insulator learning is found to provide a reference for the research and application of insulator detection. Methods:: This paper compares some of the latest deep learning models, YOLOv7, SSD, and DETR, for insulator detection based on small-sample learning. The small sample here means that the number of samples and their proportion to the total sample are relatively small. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in the natural background are selected to test the performance of these models. Results:: Tests on two public insulator image sets, InsulatorDataSet and TLP, show that the recognition rates of YOLOv7, DETR, and SSD are arranged from high to low. The DETR and the YOLOv7 have stable performance, while the SSD lacks stable performance in terms of the learning time and recognition rate. Conclusion:: The in-domain and cross-domain scenario tests show that YOLOv7 is more suitable for insulator detection under small-sample conditions among the three models.

Managerial coaching and innovative work behavior: mediating role of psychological empowerment and moderating role of task interdependence

PurposeBased on the componential theory of organizational creativity and innovation, this study examined the relationship between managerial coaching (MC) and innovative work behavior (IWB). It focused on the mediating role of psychological empowerment and the moderating role of task interdependence.Design/methodology/approachThe self-administered questionnaires were used to collect data from 420 employees of the United Arab Emirates’s public sector organizations. A hierarchical linear model (HLM) with different regression techniques was used.FindingsThe results showed that MC directly influences IWB. The path analysis also revealed that MC has an indirect effect on IWB via psychological empowerment. The moderating role of task independence in MC and IWB was also revealed.Practical implicationsThe findings shall provide insights that will help practitioners and academics understand frontline employees' innovative behavior in public sector settings and formulate strategies that will increase the involvement of employees in displaying innovation-based activities at the workplace.Originality/valueThis study adds value to the literature by integrating the componential theory of organizational creativity and innovation in public sector settings.

Data-Driven Face Mask Detection and Alert System Utilizing Haar Cascade Classifier

The COVID-19 pandemic has brought to light the pivotal role of face masks in curbing the transmission of contagious illnesses. As societies strive to uphold mask mandates in public areas, there has been a surge of interest in automated face mask detection systems. This study presents a novel data-centric methodology for crafting a reliable face mask detection and notification system leveraging the Haar cascade classifier. Our approach hinges on a meticulously curated dataset comprising images of individuals both with and without masks. Through the adept utilization of feature extraction techniques and machine learning algorithms, our system learns to discern masked faces from their unmasked counterparts in real-time scenarios. The experimental outcomes of our study attest to the efficacy of our proposed system, showcasing commendable accuracy in detecting face masks. This substantiates its viability for deployment across diverse public settings, bolstering the arsenal of public health safety measures. The seamless integration of our system stands poised to fortify efforts aimed at safeguarding community well-being amidst the persistent threat of infectious diseases. Key words: Data, Mask , Deep Learning, Hybrid.

Implementation of outpatient hip and knee arthroplasty in a multicenter public healthcare setting.

Length of hospital stay after hip and knee arthroplasty is about 1 day in Denmark with few patients discharged on the day of surgery. Hence, a protocol for multicenter implementation of discharge on day of surgery has been instituted. We aimed to describe the implementation of outpatient hip and knee arthroplasty in a multicenter public healthcare setting. We performed a prospective multicenter study from 7 public hospitals across Denmark. Patients were screened using well-defined in- and exclusion criteria and were discharged on day of surgery when fulfilling functional discharge criteria. The study period was from September 2022 to February 2023 with variable start of implementation. Data from the same centers in a 6-month period before the COVID pandemic from July 2019 to December 2019 was used for baseline control. Of 2,756 primary hip and knee arthroplasties, 37% (95% confidence interval [CI] 35-39) were eligible (range 21-50% in centers) and 52% (range 24-62%) of these were discharged on day of surgery. 21% (CI 20-23) of all patients (eligible and non-eligible) were discharged on day of surgery with a range of 10-31% within centers. This was an additional 15% (CI 13-17, P < 0.001) compared with patients discharged in the control period (6% in 2019). We found it possible to perform outpatient hip and knee replacement in 21% of patients in a public healthcare setting, probably to be increased with further center experience.

T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study.

To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.

Development of Oral Healthcare Plan for Children with Congenital Anomalies

Background: Children having birth defects denote a high-risk group for oral diseases compared to their healthy counterpart. Untreated dental caries significantly accounts for the oral disease burden of children with birth defects. A preventive oral health and restorative dental care package with close follow up could offer much benefit for such children and this poster provides some baseline information in this regard.Objective: To describe an oral health care package offered to high caries risk children with birth defects at a multi-specialty, tertiary care, and premier public dental hospital setting in Sri Lanka.Methods: A Sample of 79 children with birth defects who visited the Preventive Oral Health Unit of the National Dental Hospital (Teaching) Sri Lanka from 1st January 2017 to 29th April 2019 were included in the present analysis, by extracting data from unit’s data base. The level of corporation of a child for oral health care was assessed by child’s compliance with mouth examination and provision of simple dental treatment in a child-friendly dental setting.Results: Children with birth defects aged (mean ± SD) [6.02 ± 3.17-years], carried a high burden of untreated dental caries. The mean number (±SD) of non-cavitated decayed teeth was 2.65± 3.62, cavitated decayed teeth was 2.97±2.97 and pulp exposed teeth was 3.59 ±3.60 respectively. Children were offered a preventive oral health care package comprised of customized behavioural management, oral hygiene instructions and dietary counselling (for parental care givers), professional fluoride applications, simple restorations of carious teeth, fissure sealant applications for permanent molar teeth, referral for pulp therapy for pulp exposed symptomatic teeth under general anaesthesia to Restorative Unit B with follow up of care. 50.6% of children deemed cooperative and 8.9% were partially cooperative to receive oral health care provided by the Preventive Oral Health Unit.Conclusion: This baseline information must be utilized for effective planning of oral healthcare for children with birth defects.

Rapid online assessment of reading and phonological awareness (ROAR-PA)

Phonological awareness (PA) is at the foundation of reading development: PA is introduced before formal reading instruction, predicts reading development, is a target for early intervention, and is a core mechanism in dyslexia. Conventional approaches to assessing PA are time-consuming and resource intensive: assessments are individually administered and scoring verbal responses is challenging and subjective. Therefore, we introduce a rapid, automated, online measure of PA—The Rapid Online Assessment of Reading—Phonological Awareness—that can be implemented at scale without a test administrator. We explored whether this gamified, online task is an accurate and reliable measure of PA and predicts reading development. We found high correlations with standardized measures of PA (CTOPP-2, r = .80) for children from Pre-K through fourth grade and exceptional reliability (α = .96). Validation in 50 first and second grade classrooms showed reliable implementation in a public school setting with predictive value of future reading development.

The Ethical Principles in Ethical Guidance Documents during the COVID-19 Pandemic in the United Kingdom and the Republic of Ireland: A Qualitative Systematic Review.

The sudden onset of the coronavirus disease 2019 (COVID-19) pandemic was accompanied by a myriad of ethical issues that prompted the issuing of various ethical guidance documents for health care professionals in clinical, research, and public health settings throughout the United Kingdom (UK) of Great Britain and Northern Ireland and the Republic of Ireland. The aim of this review was to identify the main principles in ethical guidance documents published in the UK and Ireland during the COVID-19 pandemic. This review used a qualitative systematic review methodology with thematic synthesis to analyze the included ethics-related guidance documents, as defined in this review, published in the UK and Ireland from March 2020 through March 2022. The search included a general search in Google Scholar and a targeted search on the websites of the relevant professional bodies and public health authorities in the two countries. The ethical principles in these documents were analyzed using the constant comparative method (CCM). Forty-four guidance documents met the inclusion and exclusion criteria. Ten main ethical principles were identified, namely: fairness, honesty, minimizing harm, proportionality, responsibility, autonomy, respect, informed decision making, duty of care, and reciprocity. The guidelines did not present the ethical principles in equal detail. Some principles lacked definitions, leaving them vulnerable to misinterpretation by the documents' end users. Priority was frequently given to collectivist ethics over individualistic approaches. Further clarity is required in future ethical guidance documents to better guide health care professionals in similar situations.

Abstract PO2-17-08: A RETROSPECTIVE ANALYZES OF 4,885 PATIENTS OF HORMONAL RECEPTOR POSITIVE, HER2 NEGATIVE BREAST CANCER TREATED IN A REFERENCE CENTER, A REAL-WORLD DATA

Abstract Background: Hormonal receptor positive Her2 negative also described as luminal breast cancer (LBC) is the most frequent tumor corresponding to about 60-65% of cases. There is great heterogeneity in these tumors and a great difference between treatments due to lack of access, mainly in developing countries. Clinical trials are important to understand the benefit of the treatments but only 5% of the breast cancer patients are enrolled in these trials and is because of that that Real World Data (RWD) is It's becoming more and more frequent. Objectives: To evaluate LBC and describe treatments and overall survival (OS) of patients treated in public hospital in São Paulo Brazil. Methods: This was a retrospective, single cohort study, that included female patients over 18 years of age, with a diagnosis of LBC. Clinical and pathological data was collected (date of diagnose, first treatment, type of treatment, stage, type of surgery, DFS and OS). This study was approved by our local ethics committee. Results: We’ve enrolled 5,510 patients in Perola Byington ’database from 2010 to 2021. After excluding patients without minimal complete records, a total of 4,885 patients were analyzed. Most patients were diagnosed in stage I (26,9%) and II (38,2%). There was 23,4% in stage III, 3,1% in stage IV and 5,7% in stage 0. In 2,6% the of the patient’s information was not complete. Mean age at diagnosis was 57.6 years. We identified 4,761 (86.4% of 5510) patients who underwent 4,848 surgical procedures. Of the total, 47.2% were mastectomies, 50.9% partial mastectomies and 1.9% were skin sparing mastectomy. Immediate reconstruction was done in 470 patients (69.8% underwent reconstruction with an expander, 22.3% with flap rotation and 7.9% reconstruction with implants). The mean and median time between diagnosis and the beginning of treatment was analyzed, in the sample it was observed that patients who were diagnosed in stages I, II and IV had the same mean of 2.6 months, but stage IV had the lowest median 1 ,9 months. 474 patients were treated in the first line setting during the follow up, 43 % received hormone therapy (HT) and 57% received chemotherapy (CT) as first line. The duration of the treatment in first line was 17,7 months for HT and 7,7 months for CT (p &amp;lt; 0,01). OS for patients diagnosed in stages I and II did not reach the median in the available period, for patients in stages III and IV, a median of 80.4 months and 41.2 months of overall survival was identified, respectively. Conclusions: These are the first results of this large cohort of luminal patients treated in the public reference center. Most patients are diagnosed in stage I or II and more than half undergo conservative surgery. Immediate reconstruction is not routinely performed. In the public setting CDK4/6 inhibitors are not available and that led to more chemotherapy use in first line (57%) and worse survival when compared to clinical trials. Discussions on the incorporation of drugs consolidated in the literature are important to improve the treatment of advanced cancer. Citation Format: Andre Mattar, Marcelo Antonini, Marina Diogenes, Andressa Amorim, Francisco Pimentel Cavalcante, Luiz Henrique Gebrim. A RETROSPECTIVE ANALYZES OF 4,885 PATIENTS OF HORMONAL RECEPTOR POSITIVE, HER2 NEGATIVE BREAST CANCER TREATED IN A REFERENCE CENTER, A REAL-WORLD DATA [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-17-08.