Correlation between expectations of recovery and injury severity perception in whiplash-associated disorders
Bibliographic record
Abstract
OBJECTIVE: To assess the correlation between expectations of recovery and whiplash patients' perceptions of injury severity using a simplified instrument. Expectations of recovery have been shown to predict rate of recovery from whiplash injury in population-based studies. The perception of having more severe pathology or more ominous diagnostic labels has also been associated with a worse prognosis. METHODS: Consecutive patients with whiplash-associated disorder grade 1 or 2, presenting in the acute stage to a primary care centre, were asked "do you think that your injury will…" with response options "get better soon; get better slowly; never get better; don't know." Injury severity perception (ISP) was measured with a numerical rating scale which ranged from 0-10, on which subjects were asked to rate how severe (in terms of damage) they thought their injury was. The anchors were labeled "no damage" (0) and "severe, and maybe permanent damage" (10). The primary outcome measure was the correlation between the subject's ISP score and expectation of recovery. RESULTS: A total of 94 subjects (34 males, 60 females, and mean age (40.6 ± 10.0) years, range 19-60 years) were included. The initial responses to expectation of recovery were: get better soon (29/94); get better slowly (22/94); never get better (11/94); don't know (32/94). The mean ISP score was 4.9 ± 1.7 (range 2-9 out of 10). There was a high correlation between expectations and ISP scores (Spearman's rank correlation coefficient 0.68). Those who expected to recover soon and those who expected to get better slowly had the lowest ISP scores. CONCLUSIONS: The more slowly whiplash patients expect to recover, or the less sure they are of recovery, the more severe their initial perceptions of 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".