Proceeding report of the Fourth Symposium on Hidradenitis Suppurativa Advances 2019
Bibliographic record
Abstract
The Fourth Annual Symposium on Hidradenitis Suppurativa (SHSA) took place on November 1-3, 2019, at the Westin Book Cadillac Hotel in Detroit, Michigan. This symposium was a joint meeting of the US Hidradenitis Suppurativa Foundation and the Canadian Hidradenitis Suppurativa Foundation. This cross-disciplinary meeting with experts from around the world was an opportunity to discuss the most recent advances in the study of hidradenitis suppurativa (HS) pathogenesis, clinical trials, classification, scoring systems, complementary/alternative medical treatments, diet, pain management, surgical and laser treatment, and ultrasonographic assessment. A special preconference workshop was held on the use of neodymium-doped yttrium-aluminum-garnet laser hair reduction, sinus tract deroofing, and carbon dioxide laser excision with ultrasonographic mapping and tumescent anesthesia for the treatment of HS. The focused workshops on establishing an HS clinic, setting up an HS support group, the Hidradenitis Suppurativa Prospective Observational Registry and Biospecimen Repository, and wound care were held during the meeting. A special program called HS Ambassadors was established for patients who may have questions about the conference presentations, and in addition, a meet and greet for patients and HS Ambassadors was arranged. To facilitate networking between those early in their careers and clinical and research experts, a mentoring reception was held.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.036 |
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".