Use of the limits of agreement approach in periodontology.
Bibliographic record
Abstract
PURPOSE: To discuss the statistical approaches that have been traditionally used to compare measures in periodontal research, highlighting its strengths and weaknesses and, finally, to suggest the use of the limits of agreement method of Altman and Bland (1983) as an alternative method to address this question. MATERIALS AND METHODS: Using a sample dataset of clinical periodontal measures as a background, the different possible approaches for agreement assessment are discussed and statistical and clinical points are considered. Eight hundred and forty repeated measures, belonging to the training phase of a clinical study, were performed in five individuals presenting different severities of periodontal conditions. The use of correlation coefficient, comparison of means, linear regression technique, Kappa coefficient, intra-class correlation coefficient and means versus differences plot is demonstrated. RESULTS: Most of the methods are applied without the appropriate care, resulting in misleading interpretations. The information that arises from some of the methods used so far is poorly informative and adds little understanding to the operational characteristics of the raters or instruments. Some of the resulting information from the correlation coefficient and kappa coefficient may even be false or not applicable for the entire range of possible values. CONCLUSIONS: The graphical approach that plots differences against means, including the 95% limits of agreement estimated by the mean difference +/- 1.96 standard deviation of the differences is the most informative approach and its application should be considered for continuous clinical periodontal measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".