Bibliographic record
Abstract
BACKGROUND: Laser therapy is a rapidly expanding new treatment modality for onychomycosis. OBJECTIVE: To review current and prospective laser systems for the treatment of onychomycosis. METHOD: We searched the PubMed database, the Food and Drug Administration 510(k) database, ClinicalTrials.gov, and Google Scholar for in vitro studies, peer-reviewed clinical trials, manufacturers' white papers, and registered clinical trials of laser systems indicated for the treatment of onychomycosis. All published clinical trials were assessed on a 20-point methodological quality scale. RESULTS: We identified three basic science articles, five peer-reviewed articles, three white papers, and four pending clinical trials, as well as numerous gray literature documents. The overall methodological score for the clinical trials was 9.1 ± 1.1, with peer-reviewed studies showing a higher score (9.8 ± 1.5) than white papers (7.5 ± 0.7). We also identified 11 commercial laser device systems of varying global availability. CONCLUSION: Laser therapy has been tested and approved as a cosmetic treatment only for onychomycosis. It cannot be recommended as a therapeutic intervention to eradicate fungal infection at this time as more rigorous randomized, controlled trials are required to determine if laser therapy is efficacious on par with oral and topical interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".