Reliability of Dermatologists in Acne Lesion Counts and Global Assessments
Bibliographic record
Abstract
BACKGROUND: There is a paucity of data on the reliability of dermatologists in acne lesion counting and global severity assessments. The effects of training and practice on reliability are also uncertain. The objective of this study was to determine the reliability of these outcome measurements when performed by trained dermatologists. METHODS: Eleven dermatologists were divided into two groups that evaluated the same six acne subjects twice on the same day. A training session was provided either after (group A) or before (group B) the first patient evaluation sessions. Reliability of raters in lesion counting and global severity assessment was determined by calculation of intraclass correlation coefficients (ICCs). ICC values close to 1.0 indicate excellent reliability, whereas values less than 0.75 are considered unacceptable. RESULTS: Intrarater ICCs ranged from 0.37 to 0.99 for noninflammatory lesions, 0.26 to 0.97 for inflammatory lesions, and 0.56 to 0.83 for global assessments for group A (trained after); corresponding values for group B (trained before) were 0.84 to 0.98, 0.61 to 0.95, and 0.43 to 0.91. ICC values >or= 0.75 for all three outcome parameters were observed in one of six group A and three of five group B raters. Interrater ICCs for groups A and B after the first evaluation session were 0.17 versus 0.68 for noninflammatory counts, 0.84 versus 0.72 for inflammatory counts, and 0.71 versus 0.65 for global assessments, respectively. Corresponding values after session 2 were 0.79 and 0.77 for noninflammatory, 0.81 and 0.90 for inflammatory, and 0.61 and 0.77 for global assessments. CONCLUSION: Dermatologists tended to be reliable in acne lesion counting but somewhat less so in global assessments. Training tended to improve group reliability in noninflammatory lesion counts and increased the proportion of raters with good reliability in all three outcome measures. Practice enhanced reliability in all outcome measurements.
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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 | 0.037 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".