A Practical Approach to the Diagnosis and Treatment of Palmar Hyperhidrosis
Bibliographic record
Abstract
Palmar hyperhidrosis (PH), a condition characterized by excess sweating of the palms, is a common concern that presents to the plastic surgeon, which can have major impacts on patient confidence and quality of life. While several studies summarize treatment options for hyperhidrosis in general, few outline the therapeutic options available specifically for PH. Method: The authors reviewed the current literature specific to the diagnostic workup and treatment of PH. Results: In this article, we show a practical approach to managing patients presenting with PH, summarize its main nonsurgical and surgical treatment options, provide a suggested treatment ladder, and outline emerging therapeutic approaches. We suggest that, after diagnosing PH and classifying its severity, nonsurgical treatments (ie, topical antiperspirants, iontophoresis, botulinum toxin A injection, and topical/oral anticholinergics) should be utilized in a stepwise manner. In patients with severe palmar hyperhidrosis who do not respond to nonsurgical treatments, surgical intervention may be warranted, generally in the form of sympathetic denervation. Conclusion: This article provides a clear overview of PH treatment options, stepwise guidelines for physicians, and educational video resources demonstrating botulinum toxin A injections with cryotherapy and nerve blocks.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".