National Survey of Geriatricians to Define Functional Decline in Elderly People with Minor Trauma
Bibliographic record
Abstract
BACKGROUND: This study was designed to determine a clinically significant point drop in function to define functional decline and the required sensitivity for a clinical decision tool to identify elderly patients at high risk of functional decline following a minor injury. METHODS: After a rigorous development process, a survey questionnaire was administered to a random sample of 178 geriatricians selected from those registered in a national medical directory. The surveys were distributed using a modified Dillman technique. RESULTS: We obtained a satisfactory response rate of 70.5%. Ninety percent of the geriatricians required a sensitivity of 90% or less for a clinical decision tool to identify injured seniors at high risk of functional decline 6 months post injury. Our results indicate that 90% of the respondents considered a drop in function of at least 2 points in activities of daily living (ADL) as clinically significant when considering all 14 ADL items. Considering only the 7 basic ADL items, 90% of physicians considered a 1 point drop as clinically significant. CONCLUSIONS: A tool with a sensitivity of 90% to detect patients at risk of functional decline at 6 months post minor injury would meet or exceed the sensitivity required by 90% of geriatric specialists. These findings clearly define what is a clinically significant decline following a "minor injury."
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 | 0.002 | 0.006 |
| 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".