Recovery in Whiplash-Associated Disorders: Do You Get What You Expect?
Bibliographic record
Abstract
OBJECTIVE: Positive expectations predict better outcome in a number of health conditions, but the role of expectations in predicting health recovery after injury is not well understood. We investigated whether early expectations of recovery in whiplash associated disorders (WAD) predict subsequent recovery, and studied the role of "expectations" to predict recovery as determined by pain cessation and resolution of pain-related limitations in daily activities. METHODS: A cohort of 6,015 adults with traffic-related whiplash injuries was assessed, using multivariable Cox proportional hazards analysis, for association between these expectations and self-perceived recovery over a 1-year period following the injury. Recovery was assessed using 3 indices: self-perceived global recovery (primary outcome); resolution of neck pain severity; and resolution of pain-related limitations in daily activities. RESULTS: After adjusting for the effect of sociodemographic characteristics, post-crash symptoms and pain, prior health status and collision-related factors, those who expected to get better soon recovered over 3 times as quickly (hazard rate ratio = 3.62, 95% confidence interval 2.55-5.13) as those who expected that they would never get better. Findings were similar for resolution of pain-related limitations and resolution of neck pain intensity, although the effect sizes for the latter outcome were smaller. CONCLUSION: Patients' early expectations for recovery are an important prognostic factor in recovery after whiplash injury, and are potentially modifiable. Clinicians should assess these expectations in order to identify those patients at risk of chronic whiplash, and future studies should focus on the effect of changing these early expectations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".