A Novel Clinical Instrument for Predicting Delayed Recovery After Musculoskeletal Injuries
Bibliographic record
Abstract
BACKGROUND: Early identification of patients at risk for delayed recovery after an injury is important to effectively target rehabilitation. This study presents a new instrument, the Prediction of Prolonged Self-Perceived Recovery After Musculoskeletal Injuries questionnaire (PPS), for prediction of self-perceived nonrecovery after musculoskeletal injuries. METHODS: On the basis of a historic cohort (model building set, n = 557), we constructed the PPS consisting of two demographic variables (educational level and working status), a crude injury classification, and patient-rated physical and mental complaints during the acute phase of the injury. We evaluated the PPS's ability to predict self-perceived nonrecovery at 6 months in a new group of patients with minor musculoskeletal traffic-related injuries (validation set, n = 279). RESULTS: Our findings demonstrate that the PPS foresees an unfavorable course with a greater accuracy than prediction based exclusively on information about the injury. The overall percentage of correct predictions in the model building set was 77%. The overall percentage of correct predictions in the validation set was 67%. The sensitivity and specificity in relation to nonrecovery at 6 months was 55% and 73%, respectively. CONCLUSIONS: This is the first prospective clinical study in which an instrument is used for prediction purpose. On the basis of our results, we think that the PPS, even if not fully developed, can be used by clinicians as a tool for early identification of patients at risk for delayed recovery after trauma. A nonnegligible proportion of the patients who would benefit from additional rehabilitation are missed by the instrument in its present form. Further research is needed to verify our results.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".