A practical application of onychomycosis cure – combining patient, physician and regulatory body perspectives
Bibliographic record
Abstract
Due to the high relapse rates and the rise of predisposing factors, the need for curing onychomycosis is paramount. To effectively address onychomycosis, the definition of cure used in a clinical setting should be agreed upon and applied homogeneously across therapies (e.g. oral, topical and laser treatments). In order to determine what is or what should be used to define cure in a clinical setting, a literature search was conducted to identify methods used to evaluate treatment success. The limitations, strengths, prevalence and utility of each outcome measure were investigated. Seven ways to measure treatment success were identified; mycological cure, patient/investigator assessments, complete cure, quality of life instruments, severity indexes, clinical cure and temporary clearance. Despite its shortcomings, mycological cure is the most objective and consistent outcome measure used across onychomycosis studies. It is suggested that diagnostic goals of onychomycosis should be used to define cure in a clinical setting. Modifications to outcome measures such as incorporating molecular-based techniques could be a future avenue to explore.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".