An assessment of sensitivity to change of the Oral Health Impact Profile in a clinical trial
Bibliographic record
Abstract
UNLABELLED: Patient-based assessment of oral health outcomes is of growing interest. Measurement of change following clinical intervention is a key property of a health status measure. To date, most of the research on oral health status measurement has focused on construct and discriminant validity of health status measures. OBJECTIVES: The objective of this study was to assess sensitivity to change of an oral-specific health status measure, the Oral Health Impact Profile (OHIP). METHODS: Study subjects were in three groups, namely, edentulous/edentate subjects who requested and received complete implant stabilised oral prostheses (IG, n=26), edentulous/edentate subjects who requested implants but received conventional dentures (CDG1, n=22), and edentulous subjects who had new conventional complete dentures (CDG2, n=35). Data were collected pre- and post-operatively using the OHIP and a validated denture satisfaction questionnaire. RESULTS: All subjects reported similar low levels of denture satisfaction pre-operatively. Denture problems had a more significant impact on oral health-related quality of life (OHRQL) for implant seekers (IG and CDG1 subjects) than subjects seeking conventional dentures (CDG2). Following treatment, significant improvement in satisfaction with oral prostheses and OHRQL was reported by IG and CDG2 subjects; the level of improvement was more moderate for CDG1 subjects. OHIP change scores were correlated with denture satisfaction change scores. CONCLUSIONS: It was concluded that sensitivity to change of the OHIP was good. This property was not improved by using statement weights.
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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 | 0.138 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".