The Traumatic Injuries Distress Scale: A New Tool That Quantifies Distress and Has Predictive Validity With Patient-Reported Outcomes
Bibliographic record
Abstract
Study Design Observational cohort. Background Outcomes for acute musculoskeletal injuries are currently suboptimal, with an estimated 10% to 50% of injured individuals reporting persistent problems. An early risk-targeted intervention may hold value for improving outcomes. Objectives To describe the development and preliminary concurrent and longitudinal validation of the Traumatic Injuries Distress Scale (TIDS), a new tool intended to provide the magnitude and nature of risk for persistent problems following acute musculoskeletal injuries. Methods Two hundred participants recruited from emergency medicine departments and rehabilitation clinics completed the TIDS and a battery of other self-reported questionnaires. A subcohort (n = 76) was followed at 1 week and at 12 weeks after the inciting event. Exploratory factor analysis and concurrent and longitudinal correlations were used to evaluate the ability of the TIDS to predict acute presentation and 12-week outcomes. Results Exploratory factor analysis revealed 3 factors explaining 62.8% of total scale variance. Concurrent and longitudinal associations with established clinical measures supported the nature of each subscale. Scores on the TIDS at baseline were significantly associated with variability in disability, pain intensity, satisfaction, anxiety, and depression at 12 weeks postinjury, with adequate accuracy to endorse its use as part of a broader screening protocol. Limitations to interpretation are discussed. Conclusion We present the initial psychometric properties of a new measure of acute posttraumatic distress following musculoskeletal injury. The subscales may be useful as stratification variables in subsequent investigations of clinical interventions. J Orthop Sports Phys Ther 2016;46(10):920-928. Epub 3 Sep 2016. doi:10.2519/jospt.2016.6594.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".