Identification of clinically-useful cut scores of the Traumatic Injuries Distress Scale (TIDS) for predicting rate of recovery following musculoskeletal trauma
Bibliographic record
Abstract
OBJECTIVE: The Traumatic Injuries Distress Scale (TIDS) is a 12-item self-report tool intended for prognostic risk phenotyping in people with acute musculoskeletal (MSK) trauma. The initial validation study showed good associations with outcomes 12 weeks later in a cohort of 72 acutely injured patients from one region in Canada. This study aims to provide further clinical utility through identification of meaningful cut scores in a larger, mixed geography sample, and expands the prediction window from 12 to 52 weeks. METHODS: Data were drawn from databanks in London, Canada and Chicago, United States. Participants were recruited within 3 weeks of non-catastrophic MSK trauma and followed for 12 months. Using outcomes trajectories, the TIDS underwent linear regression-based analysis to predict 52-week outcomes, and area under the receiver operating characteristic curves to identify discriminative accuracy and meaningful cut scores. RESULTS: N = 224 participants with acute trauma were followed and both %Interference and Pain Severity were captured at intake and 3 follow-ups to establish curvilinear recovery trajectories. The TIDS explained significant variance in both the interference and severity outcomes after controlling for sex, region of injury, and baseline scores. ROC analysis revealed significant discriminative accuracy for predicting both the trajectories and the distal outcomes over 52 weeks. The TIDS was more accurate for identifying the low-risk than high-risk patients. CONCLUSION: The TIDS is a useful tool for 'ruling out' high risk of poor outcome in a mixed sample of participants from two different countries. IMPACT STATEMENT: The TIDS will be a useful tool for clinicians to predict the rate of recovery by displaying meaningful cut-scores for their patients after an acute musculoskeletal injury. This could lead to reduced burden of care for low risk patients and more informed treatment options for higher risk patients.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".