Primary hyperhidrosis: an updated review
Bibliographic record
Abstract
Background: Hyperhidrosis (HH) is a condition characterized by excessive sweating beyond the physiological needs of thermoregulation. HH can be classified as primary (idiopathic) hyperhidrosis (PHH) or secondary hyperhidrosis (SHH), which is associated with underlying medical conditions, medications or systemic disorders. This narrative review provides an updated overview of PHH, with a focus on epidemiology, aetiopathogenesis, clinical manifestations, diagnostic approaches and current management strategies, particularly highlighting pharmacological and procedural treatment options. Methods: A literature search was conducted in February 2025 across Ovid Medline, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL) using the key term "hyperhidrosis". The review included observational studies, clinical trials, narrative reviews, guidelines and meta-analyses published in the past 10 years. Additional references were identified through manual searches of relevant bibliographies. Results: The global prevalence of PHH is estimated to range between 0.072% and 9%, with PHH accounting for 93% of all HH cases. Whilst the precise pathophysiology remains unclear, PHH is believed to result from sympathetic overactivity, whereas SHH is associated with endocrine, neurological, infectious, malignant and medication-induced causes. PHH is diagnosed clinically and distinguishing between primary and secondary forms is essential. Management options vary based on severity, ranging from topical therapies (antiperspirants, anticholinergics), systemic medications (oral anticholinergics, adrenergic modulators), device-based interventions (iontophoresis, microwave thermolysis), injectable therapies (botulinum toxin) and surgical approaches (sympathectomy, excision, liposuction/curettage). Whilst these interventions can significantly improve symptoms and quality of life, long-term efficacy, recurrence and adverse effects remain concerns. Conclusion: PHH significantly impacts the quality life of patients contributing to both physical discomfort and psychosocial distress. An individualized, multi-modal approach is crucial to optimizing management. Further research is warranted to refine existing therapies and evaluate emerging treatment modalities for improved long-term outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.001 |
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".