Comparison of the GOHAI and OHIP‐14 as measures of the oral health‐related quality of life of the elderly
Bibliographic record
Abstract
Abstract – Objectives : This paper compares the performance of the GOHAI and the OHIP‐14 as measures of the oral health‐related quality of life of the compromised elderly. Methods: Data were obtained from a cross‐sectional survey of 225 participants, most of whom lived in a large geriatric care centre. Results: The mean age of subjects was 83 years and the majority had one or more chronic medical conditions and physical disabilities. Their main oral problems were high rates of tooth loss and xerostomia. Additive and simple count methods were used to derive GOHAI and OHIP‐14 scores. Using the additive method, 8.4% had a GOHAI score of zero and 30.3% an OHIP‐14 score of zero. Using the simple count method the percentage with a score of zero was 15.1% and 45.8%. Both measures discriminated between dentate subjects with and without one or more dentures, with and without a chewing problem and with and without dry mouth. Both also showed significant associations with self‐rated oral health and satisfaction with oral health status. Associations tended to be stronger between GOHAI scores and these variables. The measures were equally good at predicting overall psychological well‐being and life satisfaction. Although the GOHAI identified more oral functional and psychosocial impacts than the OHIP‐14, neither was markedly superior to the other when used as discriminatory measures. However, the high prevalence of subjects with zero scores may compromise the ability of the OHIP‐14 to detect within‐subject change.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".