Bibliographic record
Abstract
Self-neglect is the inability or unwillingness to provide for oneself the goods and services needed to live safely and independently. It is the most common allegation reported to Adult Protective Services agencies throughout the United States. Unfortunately, it seems that most medical examiners and their teams are not trained appropriately on self-neglect and forget to ask pertinent questions and document relevant observations. The most important aspect of self-neglect for the medical examiner is to recognize the diagnosis to avoid confusion with other forms of elder abuse, particularly neglect from a third party. In this context, a self-neglect scale could be a useful tool to assist the death investigation team. In the clinical field, a self-neglect severity scale was developed by the Consortium for Research in Elder Self-Neglect of Texas. It is here proposed that a self-neglect severity scale for medical examiners should be developed, to assist the investigative team in assessing these common cases. This scale is developed by modifying the clinical scale to adapt it to the particular needs of death investigation. This scale can help the medical examiner and his team in approaching these deaths in a systematic and comprehensive way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".