A new role for imaging in the diagnosis of physical elder abuse: results of a qualitative study with radiologists and frontline providers
Bibliographic record
Abstract
Pediatric radiologists play a key role in the detection of child abuse through the identification of characteristic injury patterns. Emergency radiologists have the potential to play an equally important role in the detection of elder physical abuse; however, they currently play little to no part in this effort. We examine the reasons behind this limited role, and potential strategies to expand it, by interviewing attending faculty from Emergency Radiology, Geriatrics, Emergency Medicine, Pediatric Radiology, and Pediatrics. Our interviews revealed that radiologists' contribution to elder abuse detection is currently limited by gaps in training, gaps in knowledge about imaging correlates, and gaps in inter-team clinical communication. Specifically, radiographic interpretation of elder trauma is severely restricted by lack of communication between frontline providers and radiologists about patients' injury mechanism and functional status. Improving this communication and re-conceptualizing ED workflow is critical to expanding and optimizing radiologists' role in elder abuse detection.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".