A Literature Review of Findings in Physical Elder Abuse
Bibliographic record
Abstract
PURPOSE: To review the medical literature for reports on the types of physical injuries in elder abuse with the aim of eliciting patterns that will aid its detection. MATERIALS AND METHODS: The databases of PubMed, CINAHL, EMBASE, and TRIP were searched from 1975 to March 2012 for articles that contained the following phrases: "physical elder abuse," "older adult abuse," "elder mistreatment," "geriatric abuse," "geriatric trauma," and "nonaccidental geriatric injury." Distribution and description of injuries in physical elder abuse from case-control studies, cross-sectional studies, case series, and case reports as seen at autopsy, in hospital emergency departments, or in medicolegal reports were tabulated and summarized. RESULTS: A review of 9 articles from a total of 574 articles screened yielded 839 injuries. The anatomic distribution in these was as follows: upper extremity, 43.98%; maxillofacial, dental, and neck, 22.88%; skull and brain, 12.28%; lower extremity, 10.61%; and torso, 10.25%. CONCLUSION: Two-thirds of injuries that occur in elder abuse are to the upper extremity and maxillofacial region. The social context in which the injuries takes place remains crucial to accurate identification of abuse. This includes a culture of violence in the family; a demented, debilitated, or depressed and socially isolated victim; and a perpetrator profile of mental illness, alcohol or drug abuse, or emotional and/or financial dependence on the victim.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".