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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.078 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".