National survey of family physicians to define functional decline in elderly patients with minor trauma
Bibliographic record
Abstract
BACKGROUND: Failing to assess elderly patients for functional decline at the time around a minor injury may result in adverse health outcomes. This study was conducted to define what constitutes clinically significant functional decline and the sensitivity required for a clinical decision instrument to identify such functional decline after an injury in previously independent elderly patients. METHODS: After a thorough development process, a survey questionnaire was administered to a random sample of 178 family physicians. The surveys were distributed using a modified Dillman technique. RESULTS: From 143 eligible surveys, we received 67 completed surveys (response rate, 46.9 %). Respondents indicated that a drop of at least 3 points on the 28-point Older Americans Resources and Services (OARS) ADL Scale was considered clinically significant by 90 % of physicians. Ninety percent (90 %) of physicians would be satisfied with a sensitivity of 90 % or more for a clinical decision instrument to detect patients at risk of functional decline at 6 months following an injury. The majority of family physicians do not routinely assess the majority of the tasks on the OARS scale for injured elderly patients. CONCLUSIONS: A high proportion of physicians (90 %) would consider a drop of 3 points on the OARS ADL Scale as significant to define functional decline and would be satisfied with a sensitivity of 90 % for a clinical decision instrument to detect such a decline. Any instrument to identify patients at elevated risk for subsequent decline should consider these outcome measures to be clinically useful.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".