What makes a tool appropriate to assess patient-reported outcomes of periodontal disease?
Bibliographic record
Abstract
CONTEXT: Patient-reported outcomes (PROs) have become primary or secondary outcome measure in clinical trials and epidemiological studies in Medicine and Dentistry in general and Periodontology in particular. PROs are patients' self-perceptions about consequences of a disease or its treatment. They can be used to measure the impact of the disease or the effect of its treatment. There are insufficient data in Periodontology related to scale development methodology although, recently, there is an increase in the number of published studies utilizing such tools in major journals. AIM: This paper is an overview of the development methodology of new PRO tools to study the impact of periodontal disease. MATERIALS AND METHODS: hypothesis enables validity assessment by hypothesis testing. The qualitative steps in item generation include literature review, focus group discussion, and key informant interviews. Expert paneling, content validity index, and pretesting are done to refine and sequence the items. Test-retest reliability, inter-rater reliability, and internal consistency reliability are assessed. The tool is administered in a representative sample to test construct validity by factor analysis. CONCLUSION: The steps involved in developing a subjective perception scale are complicated and should be followed to establish the essential psychometric properties. The use of existing tool, if it fulfills the research objective, is recommended after cross-cultural adaptation and psychometric testing.
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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 | 0.065 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".