Agreement Group Research Articles

Application of the NSE score (Neurology-Stability-Epidural compression assessment) to establish the need for surgery in spinal metastases of elderly patients: a multicenter investigation.

This retropective multicentric study aims to investigate the clinical applicability of the NSE score in the elderly, to verify the role of this tool as an easy help for decision making also for this class of patients. All elderly patients (> 65 years) suffering from spinal metastases undergoing surgical or non-surgical treatment at the authors' Institutions between 2015 and 2022 were recruited. An agreement group (AG) and non-agreement group (NAG) were identified accordingly to the agreement between the NSE score indication and the performed treatment. Neurological status and axial pain were evaluated for both groups at follow-up (3 and 6 months). The same analysis was conducted specifically grouping patients older than 75 years. A strong association with improvement or preservation of clinical status (p < 0.001) at follow-up was obtained in AG. The association was not statistically significant in NAG at the 3-month follow-up (p 1.00 and 0.07 respectively) and at 6 months (p 0.293 and 0.09 respectively). The group of patients over 75 years old showed similar results in terms of statistical association between the agreement group and better outcomes. Far from the need or the aim to build dogmatic algorithms, the goal of preserving a proper performance status plays a key role in a modern oncological management: functional outcomes of the multicentric study group showed that the NSE score represents a reliable tool to establish the need for surgery also for elderly patients.

Word-based largest chunks for Agreement Groups processing: Cross-linguistic observations

The present study reports results from a series of computer experiments seeking to combine word-based Largest Chunk (LCh) segmentation and Agreement Groups (AG) sequence processing. The AG model is based on groups of similar utterances that enable combinatorial mapping of novel utterances. LCh segmentation is concerned with cognitive text segmentation, i.e. with detecting word boundaries in a sequence of linguistic symbols. Our observations are based on the text of Le petit prince (The little prince) by Antoine de Saint-Exupéry in three languages: French, English, and Hungarian. The data suggest that word-based LCh segmentation is not very efficient with respect to utterance boundaries, however, it can provide useful word combinations for AG processing. Typological differences between the languages are also reflected in the results.