Treatment of Palmar Hyperhidrosis With Needle Injection Versus Low-Pressure Needle-Free Jet Injection of OnabotulinumtoxinA: An Open-Label Prospective Study
Bibliographic record
Abstract
BACKGROUND: OnabotulinumtoxinA (OnabotA) injections are effective to treat palmar hyperhidrosis (HH) but are quite painful. OBJECTIVE: To evaluate efficacy and pain of OnabotA injection using a needle-free jet apparatus compared with the traditional needle injection to treat palmar HH. METHODS: Twenty patients were recruited for a prospective open-label study. Their right hand was injected with 1% lidocaine with a jet injector, after which OnabotA was injected with a needle. The left hand was injected with OnabotA directly using the jet injector. Pain scores were recorded for both techniques. At 0, 1, 3, and 6 months, severity of palmar HH was evaluated with the Hyperhidrosis Disease Severity Scale (HDSS). RESULTS: One point reduction in the HDSS score at 1 month showed no statistical difference between both hands (p = .451). However, the HDSS score at 1 month from baseline dropped by 1.6 for the hand treated with traditional needle injection of OnabotA compared with 1.25 for the hand treated with jet injections (p = .031). There was no statistical difference in the pain on injection with both techniques (p = .1925). CONCLUSION: This study demonstrates effective and relatively painless use of a low-pressure jet injector for OnabotA in palmar HH.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".