Hidradenitis Suppurativa: Molecular Etiology, Pathophysiology, and Management—A Systematic Review
Bibliographic record
Abstract
Hidradenitis suppurativa is a chronic inflammatory skin condition that affects the hair follicles in areas of the body with apocrine glands. The condition is characterized by recurrent, painful nodules, abscesses, and draining sinuses that can lead to scarring and disfigurement. In this present study, we provide a focused evaluation of recent developments in hidradenitis suppurativa research, including novel therapeutics and promising biomarkers that may facilitate clinical diagnosis and treatment. We conducted a systematic review of controlled trials, randomized controlled trials, meta-analyses, case reports, and Cochrane Review articles in accordance with the PRISMA guidelines. The Cochrane Library, PubMed, EMBASE, and Epistemonikos databases were queried via Title/Abstract screen. Eligibility criteria included the following: (1) has a primary focus on hidradenitis suppurativa, (2) includes measurable outcomes data with robust comparators, (3) details the sample population, (4) English language, and (5) archived as full-text journal articles. A total of 42 eligible articles were selected for review. Qualitative evaluation identified numerous developments in our understanding of the disease's multiple potential etiologies, pathophysiology, and treatment options. It is important for individuals with hidradenitis suppurativa to work closely with a healthcare provider to develop a comprehensive treatment plan that addresses their individual needs and goals. To meet this objective, providers must keep current with developments in the genetic, immunological, microbiological, and environmental factors contributing to the disease's development and progression.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".