Isotretinoin therapy and the incidence of acne relapse: a nested case-control study: reply from authors
Bibliographic record
Abstract
Conflicts of interest: none declared. Sir, We thank Layton et al.1 for their interest in our paper.2 We would like to respond to two of their main concerns in more detail. First, Layton et al. claimed that our reported acne relapse rate of 41% overestimates the true acne relapse rate. Their reasoning is based on the fact that many patients in our cohort received subtherapeutic cumulative dosages of isotretinoin (< 2450 mg), compared with what would have been recommended for severe acne. In fact, patients receiving cumulative dosages < 2450 mg represented the minority of our cohort, where most (75%) received cumulative dosages > 2450 mg. A cumulative dosage of < 2450 mg served as the reference category in our prediction models, and is in no way the average cumulative dosage received by patients. We agree with Layton et al. that subtherapeutic cumulative dosages of isotretinoin may be associated with higher acne relapse rates, as was found in our study. However, the primary objective of our observational study was to determine the acne relapse in a population‐based cohort, reflecting actual prescribing practices, and not necessarily which dose should have been prescribed to prevent an acne relapse. This distinction is important to emphasize, as our study gives an invaluable perspective of what goes on in the usual care setting.
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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 | 0.016 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".