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 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 | 0.024 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".