A critical review of improvement rates for laser therapy used to treat toenail onychomycosis
Bibliographic record
Abstract
Onychomycosis is a nail infection that is primary caused by dermatophytes. Alternative treatments are needed as current therapies (oral and topical antifungals) have limited effectiveness. Lasers are currently approved by the FDA to temporarily increase the amount of clear nail in onychomycosis patients. Lasers can theoretically elicit fungicidal effects but in practice produce mixed results. This review compared laser-induced improvement rates to FDA-approved indications and traditional onychomycosis treatments. A review of the literature (PubMed, Clinicaltrials.gov, Medline and Embase) was used to locate articles for this review. RCTs, non-randomized, uncontrolled and retrospective studies that included at least one of the following measures were eligible; complete cure, mycological cure, clinical improvement and clinical cure. Mycological cure (negative culture and negative microscopy) was evaluated in two studies using patients as the unit of analysis with an average rate of 11%, increasing to 63% when nails were used as the unit of analysis (three studies). Clinical cure (100% clear nail) was evaluated in six studies with a rate of 13% using nails as the unit of analysis and 13% when patients were used as the unit of analysis (two studies). Clinical improvement (at any time point) was found in 36% of patients (five studies) and 67% of nails (nine studies). Nail clarity as measured by clear nail growth and/or nail plate/bed clearance at 12 weeks was found to be 2.6 mm across onychomycotic nails. Laser studies, to date, provide preliminary evidence of clinical improvement and clear nail growth in toenail onychomycosis, consistent with the FDA clearance for aesthetic endpoints. Laser studies however do not provide efficacy rates for medical endpoints that equate or exceed those found with traditional therapies (oral and topical treatments).
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".