A Comprehensive Approach to the Recognition, Diagnosis, and Severity-Based Treatment of Focal Hyperhidrosis: Recommendations of the Canadian Hyperhidrosis Advisory Committee
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it