Observer agreement in the grading of oral epithelial dysplasia
Bibliographic record
Abstract
OBJECTIVES: To determine the extent of observer agreement in diagnosis of oral epithelial dysplasia (OED). Published studies of OED examiner agreement report relatively low agreement levels; however, these studies were limited by the methodologies employed. METHODS: For this study, 64 slides were each independently examined twice by three oral pathologists. Consistency was assessed by determining intra- and interexaminer agreement. Conformity was assessed by using the modal diagnosis as a gold standard. RESULTS: The group showed moderate interobserver agreement when grading the presence or absence of OED with a group-simple kappa (Ks) of 0.51 (95% CI = 0.42-0.61), and substantial agreement when using a 5-point ordinal scale with a group-weighted kappa (Kw) of 0.74 (95% CI = 0.64-0.85). The group showed fair to substantial intraexaminer agreement when assessing the presence or absence of OED, with Ks ranging from 0.22 to 0.78, and showing almost a perfect agreement using a 5-point ordinal scale, with Kw ranging from 0.82-0.96. Conformity with the comparison standard modal diagnosis was almost perfect, with pairwise Kw ranging from 0.81 to 0.92. CONCLUSION: Overall, there was substantial intra- and interobserver consistency and almost perfect conformity in the grading of OED. Appropriate statistical methods are necessary to determine the degree of observer agreement.
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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 | 0.074 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".