Applying the phenotype approach for rosacea to practice and research
Bibliographic record
Abstract
This thoughtful article has been presented by a group of authors from departments in Canada, Sweden and the USA. The central point of this article is the case for a different approach to the classification of the skin disease, rosacea, although the argument is also relevant to other diseases. Currently rosacea is divided into a number of different subtypes based on presence or absence of flushing or transient redness of the face, swelling of the nose, small spots (without or without heads) and broken veins or telangiectases. The authors are arguing for a less restricted approach which uses a combination of different terms, some of which are regarded as major and others as minor, that together best describe the changes on the skin that each patient shows to account for the fact that patients may have several such changes at the same time or one may evolve into another. It also allows greater flexibility. Adopting this “phenotype” approach, and thereby taking into account all the features of the condition, would allow better assessment of the effects of treatment on each patient, in clinical trials, or in understanding the disease, or the impact of rosacea on a patient's life. How we describe things in medicine often limits our ability to understand disease and its treatment and impact. This paper argues for greater flexibility in using what we see to classify illness.
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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 | 0.148 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.006 | 0.059 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".