Real-World Efficacy of 1064-nm Nd:YAG Laser for the Treatment of Onychomycosis
Bibliographic record
Abstract
Background: Onychomycosis is a cosmetic and, at times, medical concern; therefore, effective and safe alternatives to treatment are needed. Objective: To determine the efficacy of a 1064-nm Nd:YAG laser for the treatment of onychomycosis in a real-world setting. Methods: A single-centre retrospective chart review was conducted between 2012 and 2013. One hundred consecutive patients with a culture- and/or potassium hydroxide–confirmed diagnosis of onychomycosis were treated at least twice. Baseline and follow-up photographs were taken, and the change in degree of clinical nail involvement of the subject’s great toenail was determined by a blinded reviewer using validated planimetry measurement. Results: A total of 199 hallux nails from 100 subjects were assessed. The mean infected area decreased from 53.2% at baseline to 50.8% at the end of the study (paired t test, P = .054; Wilcoxon signed rank test, P = .006). Degree of nail involvement was statistically significantly associated with amount of improvement; subjects who had the greatest degree of nail involvement improved the most, while those with less severe disease showed a worsening of nail appearance (Kruskal-Wallis test, P < .001). Three-quarters (72.6%) of nails that had more than 67% nail involvement showed statistically significant improvement (χ 2 test, P = .001). Adverse events were limited to mild to moderate pain at the time of therapy. A total of 76 subjects were assessed for treatment satisfaction: 60% were very satisfied with treatment despite limited clinical improvement in some cases. Conclusions: Laser therapy has a very limited positive clinical effect on the appearance of onychomycosis after 2 treatment sessions.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".