Computerised adaptive testing accurately predicts CLEFT-Q scores by selecting fewer, more patient-focused questions
Bibliographic record
Abstract
BACKGROUND: The International Consortium for Health Outcome Measurement (ICHOM) has recently agreed upon a core outcome set for the comprehensive appraisal of cleft care, which puts a greater emphasis on patient-reported outcome measures (PROMs) and, in particular, the CLEFT-Q. The CLEFT-Q comprises 12 scales with a total of 110 items, aimed to be answered by children as young as 8 years old. OBJECTIVE: In this study, we aimed to use computerised adaptive testing (CAT) to reduce the number of items needed to predict results for each CLEFT-Q scale. METHOD: We used an open-source CAT simulation package to run item responses over each of the full-length scales and its CAT counterpart at varying degrees of precision, estimated by standard error (SE). The mean number of items needed to achieve a given SE was recorded for each scale's CAT, and the correlations between results from the full-length scales and those predicted by the CAT versions were calculated. RESULTS: Using CATs for each of the 12 CLEFT-Q scales, we reduced the number of questions that participants needed to answer, that is, from 110 to a mean of 43.1 (range 34-60, SE < 0.55) while maintaining a 97% correlation between scores obtained with CAT and full-length scales. CONCLUSIONS: CAT is likely to play a fundamental role in the uptake of PROMs into clinical practice given the high degree of accuracy achievable with substantially fewer items.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".