A Comprehensive Approach to the Recognition, Diagnosis, and Severity-Based Treatment of Focal Hyperhidrosis: Recommendations of the Canadian Hyperhidrosis Advisory Committee
Bibliographic record
Abstract
BACKGROUND: Hyperhidrosis can have profound effects on a patient's quality of life. Current treatment guidelines ignore disease severity. OBJECTIVE: The objective was to establish clinical guidelines for the recognition, diagnosis, and treatment of primary focal hyperhidrosis. METHODS AND MATERIALS: A working group of eight nationally recognized experts was convened to develop the consensus statement using an evidence-based approach. RECOMMENDATIONS: An algorithm was designed to consider both disease severity and location. The Hyperhidrosis Disease Severity Scale (HDSS) provides a qualitative measure that allows tailoring of treatment. Mild axillary, palmar, and plantar hyperhidrosis (HDSS score of 2) should initially be treated with topical aluminum chloride (AC). If the patient fails to respond to AC therapy, botulinum toxin A (BTX-A; axillae, palms, soles) and iontophoresis (palms, soles) should be the second-line therapy. In severe cases of axillary, palmar, and plantar hyperhidrosis (HDSS score of 3 or 4), both BTX-A and topical AC are first-line therapy. Iontophoresis is also first-line therapy for palmar and plantar hyperhidrosis. Craniofacial hyperhidrosis should be treated with oral medications, BTX-A, or topical AC as first-line therapy. Local surgery (axillary) and endoscopic thoracic sympathectomy (palms and soles) should only be considered after failure of all other treatment options. CONCLUSIONS: These guidelines offer a rapid method to assess disease severity and to treat primary focal hyperhidrosis according to severity.
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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 | 0.064 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".