New Tools and Insights: Hyperhidrosis in a Community Setting
Bibliographic record
Abstract
Background: While excessive sweating is considered by many to be a benign condition, the physical and psychosocial impact it can have on hyperhidrosis sufferers is not always fully appreciated. Methods: A retrospective review of our pharmacy's patient records with the presenting complaint of sweating was conducted. The review covered a 2-year span and consisted of 2517 records. Using a structured questionnaire, patient information was collected via e-mail, as well as telephone and face-to-face interviews by pharmacists. Patients described their difficulties and the different coping methods they would employ to alleviate or hide their excessive sweating on different areas such as the underarms, face/scalp, hands, feet, and torso/groin. Results: Underarm sweating is the most frequent area for which both male and female patients seek help, followed by hands, feet, and face/neck. For males over 40, face and neck sweating is the most frequent area of concern, whereas for females over 40, underarms remain a top priority. Patients report numerous instances of feeling isolated, depressed, and a lack of self-confidence in all areas of professional and social life. Conclusion: It is important to recognize that for some patients, excessive sweating can be a severely debilitating condition. Appropriate action must be taken in order to improve the patient's quality of life.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".