Minimally important difference of the Child Oral Health Impact Profile for children with orofacial anomalies
Bibliographic record
Abstract
BACKGROUND: The Child Oral Health Impact Profile (COHIP) is an instrument designed to measure the self-reported oral health-related quality of life of children between the ages of 8 and 15, including domains for oral health, functional well-being, social-emotional well-being, school environment and self-image. The purpose of this study was to estimate the minimally important difference (MID) of the COHIP for patients with cleft lip/palate. METHODS: Data from a 6-year, prospective, longitudinal cohort study of children with cleft lip/palate were analyzed to estimate the MID. Analysis was restricted to patients with data at baseline and first follow-up and not receiving a surgical intervention in the intervening years (N = 281). MIDs were estimated via the anchor-based method, using the Global Assessment of Change, and the effect size distribution method. RESULTS: Based on the distributional method, the minimally important differences were 0.16 (oral health), 0.12 (functional), 0.22 (social-emotional), 0.21 (school environment) and 0.19 (self-image). MID anchor estimates for COHIP domains ranged from -0.32 to 0.84. The anchor-based and effect size MID estimates for the overall COHIP score were 2.95 and 0.25, respectively. CONCLUSION: The minimally important difference of the Child Oral Health Impact Profile is recommended for interpreting clinically meaningful change in patients with cleft lip/palate.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".