Evaluation of PCR for the diagnosis of dermatophytes in nail specimens from patients with suspected onychomycosis
Bibliographic record
Abstract
BACKGROUND: Conventional methods for detecting fungi in nail specimens are either nonspecific (microscopy) or insensitive (culture). Recently, PCR has been used to improve sensitivity in detecting the causative fungi in nail specimens from patients with suspected onychomycosis. AIM: To compare the detection rates of PCR with those of microscopy (with potassium hydroxide; KOH) and culture for dermatophytes in nail specimens from patients with suspected onychomycosis. METHODS: In total, 120 patients with clinically suspected onychomycosis were recruited, and using a topoisomerase II-based PCR, we compared the detection rate of dermatophytes for the three methods. RESULTS: KOH microscopy, culture and PCR respectively yielded positive rates of 35 (29.2%), 12 (10%) and 48 (40%), and negative rates of 85 (70.8%), 108 (90%) and 72 (60%). Two culture-positive specimens were not detected by PCR, but PCR picked up 38 specimens missed by culture. Of the 35 specimens that were microscopy-positive, 12 grew dermatophytes and 23 nondermatophytes. CONCLUSIONS: This study demonstrates that PCR has a higher positive and lower negative rate for detection of dermatophytes compared with KOH microscopy or culture. We suggest that PCR should be used as a complementary method for confirmation of clinically suspected dermatophytic onychomycosis.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".