The most recent advances in understanding and managing hidradenitis suppurativa
Bibliographic record
Abstract
Hidradenitis suppurativa (HS) is a chronic, inflammatory, recurrent, and debilitating skin disease of the hair follicle unit that typically develops after puberty. HS has a significant negative impact on both the quality of life (QOL) of patients affected by this disease as well as family members and caregivers. However, the pathogenesis of HS is multifactorial and still remains to be fully elucidated, which makes the development of treatments difficult. The last 10 years have seen a surge in HS research, and many new findings have come to light, yet much more remains to be elucidated. Physicians must employ a multidisciplinary approach to maximally address all facets of HS. Clinical characteristics of the disease that differ between females and males as well as across different races and ethnic groups must be considered. Targeted topical, oral, and injectable therapies continue to be developed for HS as a greater understanding of the pathogenesis is reached. However, randomized controlled trials regarding dietary factors that may contribute to HS are needed to meet our patients' growing concerns and questions about the role of diet in HS pathogenesis. Finally, improved outcome measures are needed to standardize HS severity and grading between physicians and clinical trials, and a more diverse representation of HS populations is needed in clinical trials.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".