Rasch Analysis of Patient- and Parent-Reported Outcome Measures in the International Consortium for Health Outcomes Measurement Standard Set for Cleft Lip and Palate
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the psychometric performance of the patient- and parent-reported measures in the International Consortium for Health Outcomes Measurement (ICHOM) Standard Set for Cleft Care, and to identify ways of improving concept coverage. METHODS: Data from 714 patients with cleft lip and/or palate, aged 8 to 9, 10 to 12.5, and 22 years were collected between November 2015 and April 2019 at Erasmus University Medical Center, Boston Children's Hospital, Duke Children's Hospital, and from participating sites in the CLEFT-Q Phase 3 study. The Standard Set includes 9 CLEFT-Q scales, the Nasal Obstruction Symptom Evaluation (NOSE) questionnaire, the Child Oral Health Impact Profile-Oral Symptoms Scale (COHIP-OSS), and the Intelligibility in Context Scale (ICS). Targeting, item-fit statistics, thresholds for item responses, and measurement precision (PSI) were analyzed using Rasch measurement theory. RESULTS: The proportion of the sample to score within each instruments range of measurement varied from 69% (ICS) to 92% (CLEFT-Q teeth and COHIP-OSS). Specific problems with individual items within the NOSE and COHIP-OSS questionnaires were noted, such as poor item fit to the Rasch model and disordered thresholds (6 of 10). Reliability measured with PSI was above 0.82 for the ICS and all but one CLEFT-Q scale (speech distress). PSIs were lowest for the COHIP-OSS (0.43) and NOSE questionnaire (0.35). CONCLUSION: The patient- and parent-reported components within the facial appearance, psychosocial function, and speech domains are valid measures; however, the facial function and oral health domains are not sufficiently covered by the CLEFT-Q eating and drinking, NOSE, and COHIP-OSS, and these questionnaires may not be accurate enough to stratify cleft-related outcomes.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".