Recovery from acute injury: Clinical, methodological and philosophical considerations
Bibliographic record
Abstract
BACKGROUND: The foundational basis for rehabilitation of musculoskeletal injury as a profession rests upon the ability of an organism to recover from a deviation in homeostasis, and the clinician's ability to influence that process. Much work has been done in an effort to describe and predict recovery from acute injury, in particular soft tissue injuries of the spine (whiplash and low back pain). Recent reviews have identified inconsistencies in the criteria for identifying recovery in this literature that hamper attempts at knowledge translation. PURPOSE: This article is intended to stimulate discussion around a new, standardised and acceptable set of criteria for discriminating between the injured individual who reaches a satisfying end to the experience of injury and the individual who does not reach that end. CONCLUSIONS: Self-discrepancy theory and self-determination theory are used to frame the discussion. It is hoped that the introduction of a new paradigm will lead to the development of more standardised, acceptable and useful outcomes, and will facilitate data synthesis from studies on prognosis and intervention for acute musculoskeletal injury.
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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 | 0.575 | 0.575 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".