The future of patient-reported outcome measurement in hyperhidrosis lies in thoughtful, evidence-based implementation
Bibliographic record
Abstract
Hyperhidrosis, a condition characterized by excessive sweating, consists of the following two main types: primary focal hyperhidrosis, which is idiopathic and affects localized areas (e.g. palms, soles, underarms), and secondary generalized hyperhidrosis, which is often associated with medical conditions and/or medications, resulting in generalized sweating.1 Hyperhidrosis impacts how patients feel and function, which ultimately impacts their quality of life.2 These concepts are best captured from the patient’s perspective, using self-report instruments known as patient-reported outcome measures (PROMs). The benefits of PROMs are well-documented; they improve communication between patients and providers, improve the measurement of key clinical outcomes and provide insights into treatment impacts.3 Selecting a high-quality PROM tailored to a specific clinical setting is essential, as not all PROMs are developed and/or validated to the same standard. High-quality PROMs adhere to COSMIN standards, which assess criteria including validity, reliability and responsiveness.4 The Hyperhidrosis Quality of Life Index (HidroQoL©), a PROM specifically developed for hyperhidrosis, has demonstrated acceptable measurement properties in previous studies.5,6 In a study by Donhauser et al., the measurement properties of HidroQoL are further validated, with evidence that it can detect meaningful changes in individual patients over time, improving interpretability, particularly for healthcare providers tracking patient progress over time.7
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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 | 0.508 | 0.630 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".