Can diagnostic imaging help improve elder abuse detection?
Bibliographic record
Abstract
Elder abuse is an underdetected, under-reported issue with severe consequences. Its detection presents unique challenges based on characteristics of this vulnerable population, including cognitive impairment, age-related deconditioning, and an increased number of co-morbidities, all of which predispose to increase vulnerability to injury. While radiologists play a critical role in detection of child abuse, this role is currently not paralleled in detection of elder abuse. We conducted a thorough review of the literature using MEDLINE to describe the current knowledge on injury patterns and injury findings seen in elder abuse, as well as barriers to and recommendations for an increased role of diagnostic imaging in elder abuse detection. Barriers limiting the role of radiologists include lack of training and paucity of rigorous systematic research delineating distinctive imaging findings for physical elder abuse. We outline the current ways in which imaging can help raise clinical suspicion for elder abuse, including inconsistencies between purported mechanism of injury and imaging findings, injury location, multiple injuries at differing stages of healing, and particular patterns of injury likely to be intentionally inflicted. We additionally outline the mechanism by which medical education and clinical workflow may be modified to increase the role for imaging and radiologist participation in detecting abuse in older adult patients, and identify potential future directions for further systematic research.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".