Determining the minimally important difference for the Oral Health Impact Profile‐20
Bibliographic record
Abstract
In the context of clinical trials, measurement of change is critical. The aim of this study was to determine the minimally important difference (MID) for the Oral Health Impact Profile-20 (OHIP-20) when used with partially dentate patients undergoing treatment that included the provision of removable partial dentures. In a prospective clinical trial, 51 consecutive patients were provided with removable partial dentures. In addition to demographic and dental status data, patients completed an OHIP-20 prior to treatment. One month postoperatively, patients completed a post-treatment OHIP-20 and a global transition scale. Domains assessed in the global transition scale were appearance, ability to chew food, oral comfort, and speech. The MID for the OHIP-20 was calculated using the anchor-based approach. From the initial sample of 51 patients, 44 completed post-treatment questionnaires and were included in the analysis. Change scores in the four transition domains indicated that new dentures had a positive impact in the majority of subjects, especially in perceived impact on chewing and appearance. The study provided a guideline as to what constitutes the MID for the OHIP-20. This benchmark can be used when interpreting the impact of clinical intervention for replacing missing teeth and for power calculation in statistical analyses.
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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 | 0.112 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".