A Short Version of the International Hip Outcome Tool (iHOT‐12) for Use in Routine Clinical Practice
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to develop and validate a shorter version of the 33-item International Hip Outcome Tool (iHOT-33) that could be easily used in routine clinical practice to measure both health-related quality of life and changes after treatment in young, active patients with hip disorders. METHODS: A development dataset (104 patients) was explored with forward-selection linear regression analysis to choose a reduced item set for the new scale. This was tested in a validation dataset (1,833 patients) and responsiveness subset (80 patients) to measure agreement between the shorter and longer versions and to test the sensitivity of the shorter instrument to change after treatment. RESULTS: Twelve items were chosen for a short version of the International Hip Outcome Tool (iHOT-12). The iHOT-12 showed excellent agreement with the long version (iHOT-33). It captured 95.9% (95% confidence interval, 95.0% to 96.8%) of the variation of the iHOT-33 and showed equivalent sensitivity to change with a standardized effect size of 0.98 (95% confidence interval, 0.67 to 1.28). CONCLUSIONS: A short version of the International Hip Outcome Tool (iHOT-12) has been developed. It has very similar characteristics to the original rigorously validated 33-item questionnaire, losing very little information despite being only one-third the length. It is valid, reliable, and responsive to change. We suggest that it be used for initial assessment and postoperative follow-up in routine clinical practice.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".