Development and preliminary validation of a Function IndeX for Trauma (FIX-IT)
Bibliographic record
Abstract
BACKGROUND: Assessing fracture healing in clinical trials is subjective. The new Function IndeX for Trauma (FIX-IT) score provides a simple, standardized approach to assess weight-bearing and pain in patients with lower extremity fractures. We conducted an initial validation of the FIX-IT score. METHODS: We conducted a cross-sectional study involving 50 patients with lower extremity fractures across different stages of healing to evaluate the reliability and preliminary validity of the FIX-IT score. Patients were independently examined by 2 orthopedic surgeons, 1 orthopedic fellow, 2 orthopedic residents and 2 research coordinators. Patients also completed the Short Form-36 version 2 (SF-36v2) questionnaire, and convergent validity was tested with the SF-36v2. RESULTS: For interrater reliability, the intraclass correlation coefficients ranged from 0.637 to 0.915. The overall interrater reliability for the total FIX-IT score was 0.879 (95% confidence interval 0.828-0.921). The correlations between the FIX-IT score and the SF-36 ranged from 0.682 to 0.770 for the physical component summary score, from 0.681 to 0.758 for the physical function subscale, and from 0.677 to 0.786 for the role-physical subscale. CONCLUSION: The FIX-IT score had high interrater agreement across multiple examiners. Moreover, FIX-IT scores correlate with the physical scores of the SF-36. Although additional research is needed to fully validate FIX-IT, our results suggest the potential for FIX-IT to be a reliable adjunctive clinician measure to evaluate healing in lower extremity fractures. LEVEL OF EVIDENCE: Diagnostic Study Level I.
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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) | 0.000 | 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".