Discomfort during Periorbital and Lateral Temporal Laser Vein Treatment
Bibliographic record
Abstract
BACKGROUND: Treatments for cosmetically unpleasing periocular and lateral temporal veins are limited. The purpose of this study was to test the hypothesis that the application of topical lidocaine before the cosmetic treatment of periorbital and lateral temporal veins with a neodymium-doped yttrium aluminum garnet (ND:YAG) laser will result in a significant reduction in subjective pain compared with placebo as assessed using a visual analogue scale. METHODS: Twenty patients who required bilateral treatment of facial veins were randomly assigned to receive either placebo or 30% lidocaine gel applied topically over the veins, a split-body design. Both the investigator and the patient were blinded to the treatment. An ND:YAG laser was used to treat the veins. Patients completed a visual analogue scale to assess the pain on each side of the face. Data were analyzed using nonparametric data testing. RESULTS: There was a 64.0% reduction in pain on the treatment side compared with the placebo side (P < 0.001). There was no significant difference in patient-assessed subjective efficacy between sides (P = 0.2). Complications were minimal and mild. CONCLUSIONS: Patients undergoing periorbital and temporal vein ablation using ND:YAG laser should be offered topical lidocaine as the pain levels are moderate. The use of topical 30% lidocaine results in a significant reduction in pain levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".