The Development, Deployment, and Evaluation of the CLEFT-Q Computerized Adaptive Test: A Multimethods Approach Contributing to Personalized, Person-Centered Health Assessments in Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: Routine use of patient-reported outcome measures (PROMs) and computerized adaptive tests (CATs) may improve care in a range of surgical conditions. However, most available CATs are neither condition-specific nor coproduced with patients and lack clinically relevant score interpretation. Recently, a PROM called the CLEFT-Q has been developed for use in the treatment of cleft lip or palate (CL/P), but the assessment burden may be limiting its uptake into clinical practice. OBJECTIVE: We aimed to develop a CAT for the CLEFT-Q, which could facilitate the uptake of the CLEFT-Q PROM internationally. We aimed to conduct this work with a novel patient-centered approach and make source code available as an open-source framework for CAT development in other surgical conditions. METHODS: CATs were developed with the Rasch measurement theory, using full-length CLEFT-Q responses collected during the CLEFT-Q field test (this included 2434 patients across 12 countries). These algorithms were validated in Monte Carlo simulations involving full-length CLEFT-Q responses collected from 536 patients. In these simulations, the CAT algorithms approximated full-length CLEFT-Q scores iteratively, using progressively fewer items from the full-length PROM. Agreement between full-length CLEFT-Q score and CAT score at different assessment lengths was measured using the Pearson correlation coefficient, root-mean-square error (RMSE), and 95% limits of agreement. CAT settings, including the number of items to be included in the final assessments, were determined in a multistakeholder workshop that included patients and health care professionals. A user interface was developed for the platform, and it was prospectively piloted in the United Kingdom and the Netherlands. Interviews were conducted with 6 patients and 4 clinicians to explore end-user experience. RESULTS: The length of all 8 CLEFT-Q scales in the International Consortium for Health Outcomes Measurement (ICHOM) Standard Set combined was reduced from 76 to 59 items, and at this length, CAT assessments reproduced full-length CLEFT-Q scores accurately (with correlations between full-length CLEFT-Q score and CAT score exceeding 0.97, and the RMSE ranging from 2 to 5 out of 100). Workshop stakeholders considered this the optimal balance between accuracy and assessment burden. The platform was perceived to improve clinical communication and facilitate shared decision-making. CONCLUSIONS: Our platform is likely to facilitate routine CLEFT-Q uptake, and this may have a positive impact on clinical care. Our free source code enables other researchers to rapidly and economically reproduce this work for other PROMs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".