Item reduction of the patient-rated wrist evaluation using decision tree modelling
Bibliographic record
Abstract
Background: The aim of this study is to assess the viability of a decision tree version of an often used questionnaire to measure wrist pain and disability, the Patient Rated Wrist Evaluation.Methods: Patient Rated Wrist Evaluation scores were collected from a cohort of 10394 patients who are part of a routine outcome measurement system. A decision tree version of the Patient Rated Wrist Evaluation (PRWE) was created. The intraclass correlation was used to evaluate the inter-version reliability between the original PRWE and the decision tree version.Results: The decision tree reduced the number of questions from 5 to 3 for the pain subscale, and from 10 to 3 for the disability subscale. The intraclass correlation between the original PRWE and the decision tree version was 0.97. The mean difference between the Patient Rated Wrist Evaluation and the decision tree Patient Rated Wrist Evaluation total sumscore was 0.35 (95% CI −9.92–10.62).Conclusions: We found that the decision tree was successful at reducing the items of the Patient Rated Wrist Evaluation from fifteen to only six questions with very high similarity to the scores of the full questionnaire.Implications for rehabilitationThe Patient Rated Wrist Evaluation can reliably be used with 6 instead of 15 questions.Decision trees are useful statistical tools to shorten lengthy questionnaires, especially when large amounts of data are available.Having a shortened Patient Rated Wrist Evaluation saves patients and clinicians time in answering this specific questionnaire.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".