Comparing the diagnostic accuracy of <scp>PCR</scp>‐reverse blot hybridization assay and conventional fungus study in superficial fungal infection of the skin: A systematic review
Bibliographic record
Abstract
BACKGROUND: In superficial fungal infections, prompt diagnosis and treatment are essential to prevent the spread of infection and minimise the impact on patients' quality of life. Traditional diagnostic methods, such as KOH smear and fungal culture, have limitations in terms of sensitivity and turnaround time. Recently, the PCR-reverse blot hybridization assay (PCR-REBA) has been developed for the direct detection of dermatophyte DNA. However, there is a lack of information assessing the diagnostic accuracy of PCR-REBA. OBJECTIVES: This systematic review aimed to evaluate the diagnostic accuracy of PCR-REBA in superficial fungal infections compared to conventional and molecular methods. METHODS: The comprehensive search containing Ovid MEDLINE and Embase databases was conducted on 7 August 2022. Two reviewers independently reviewed the included articles. Quality assessment was performed using the Newcastle-Ottawa Scale tool. RESULTS: The included studies were conducted in Korea (five studies) and the Netherlands (two studies), all of which were conducted in a single institution. The quality assessment of these studies indicated low risk of bias. When compared to the potassium hydroxide (KOH) smear and fungus culture, the sensitivity of PCR-REBA ranged from 85% to 100%, and the positive predictive values ranged from 58.9% to 100%. When compared to the RT-PCR, the sensitivity of PCR-REBA ranged from 93.3% to 100%, and the positive and negative predictive values were 91.6%-99.6% and 81.0%-89.1%, respectively. CONCLUSIONS: The PCR-REBA shows promise as a valuable diagnostic tool for dermatophytosis, offering practical and cost-effective benefits.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".