Does recall of preinjury disability change over time?
Bibliographic record
Abstract
BACKGROUND: Pre-injury disability must be determined when assessing whether treatment programs return people to pre-injury status, however there is little empirical evidence to support recommendations that this be done as soon as possible after injury to prevent recall bias. OBJECTIVES: To determine disagreement between recall of pre-injury disability at different time points post-injury and bias towards under- or overestimating pre-injury disability. METHODS: Self-reported pre-injury global disability was assessed within days, 6 months and 12 months post-injury in patients admitted to two level 1 adult trauma centres. Kappa statistics and multiple logistic regression models identified predictors of disagreement between time-points. RESULTS: Pre-injury disability was measured at all time-points in 801 patients. Pre-injury disability at baseline was rated as none, mild, moderate, marked and severe in 80%, 12%, 5.1%, 1.9% and 1.0% respectively. Absolute agreement between baseline and 6 and 12 months respectively, was 79% and 80%. Corresponding kappa values (95% confidence intervals) were 0.33 (0.26-0.40) and 0.32 (0-25-0.38). Patients over 65 years or not completing high school were more likely to report less pre-injury disability at 6 and 12 months than at baseline with adjusted odds ratios (95% confidence intervals) for these groups being 8.24 (4.32-15.72) and 1.93 (1.03-3.64) respectively. CONCLUSIONS: There was little evidence of recall bias in an adult trauma population if self-reported global pre-injury disability was assessed 6 months post-injury. The recall of pre-injury disability up to 6 months post-injury can be used to determine return to pre-injury status, if assessment is not feasible shortly after injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".