Shortcomings in rosacea diagnosis and classification
Bibliographic record
Abstract
In 2002, the National Rosacea Society (NRS) proposed a provisional classification for rosacea based on the clinical knowledge of that time and on morphological features.1 Explicit was the intent that this was ‘a framework that could be readily updated and expanded as new discoveries were made’.1 That scheme posited primary and secondary criteria for diagnosis and division into four subtypes, representing common clinical patterns of presentation, and one variant.1 It also helped to increase recognition of rosacea as a disease and to guide research. Subsequent incorporation of this paradigm in epidemiological, pathophysiological and translational research has provided for greater standardization in rosacea reporting. Now, after more than a decade of using this scheme in research and clinical practice, it should be re‐evaluated to incorporate current scientific knowledge and address shortcomings in guiding diagnosis and classification of rosacea. Unequivocal diagnoses can be challenging in the absence of an absolute gold standard (histology in malignancy; reduced ejection fraction in congestive heart failure; positive bacterial blood cultures in sepsis). Accuracy of a diagnostic test is based on the proportion of true results of the test (true positive, or sensitivity; and true negative, or specificity) in a population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".