A Standard Set for Outcome Measurement in Patients With Hand and Wrist Conditions: Consensus by the International Consortium for Health Outcomes Measurement Hand and Wrist Working Group
Bibliographic record
Abstract
PURPOSE: To describe the principles, process, and results of creating the International Consortium for Health Outcomes Measurement (ICHOM) standard set for hand and wrist conditions. METHODS: Following the standardized methods of ICHOM, an international working group of hand surgeons, therapists, and researchers was assembled to develop an evidence-based, patient-centered, standard set of outcome measures for patients with hand and wrist conditions. Multiple systematic reviews were performed to support our choices of outcome domains and tools for hand and wrist conditions. Fourteen video conferences were held between March 2018 and March 2020, and a modified Delphi process was used. RESULTS: A consensus was reached on 5 measurement tracks: the thumb, finger, wrist, nerve, and severe hand trauma tracks, with a distinction between regular and extended tracks for which specific allocation criteria applied. The standard set contains a selection of outcome tools and predefined time points for outcome measurement. Additionally, we developed a hierarchy for using the tracks when there are multiple conditions, and we selected risk-adjustment, case-mix variables. CONCLUSIONS: The global implementation of the ICHOM standard set for hand and wrist conditions may facilitate value-based health care for patients with hand and wrist conditions. CLINICAL RELEVANCE: The ICHOM standard set for hand and wrist conditions can enable clinical decision making, quality improvement, and comparisons between treatments and health care professionals.
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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 | 0.510 | 0.538 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".