The diagnosis of mucormycosis by PCR in patients at risk: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: This systematic review and meta-analysis aimed to examine the performance of polymerase chain reaction (PCR) assays for diagnosing mucormycosis. Methods: A standardised search was conducted from conception to December 3rd 2024 using PubMed, Embase, Global Health, and Cochrane library. Original studies that used PCR-based methods on any human specimen to diagnose mucormycosis were analysed for eligibility. Using a bivariate meta-analysis, the diagnostic performance of PCR was examined against the European Organisation for Research and Treatment of Cancer-Mycoses Study Group Education and Research Consortium 2020 (EORTC-MSGERC) definitions of proven and probable invasive mould disease, which was modified to include all patients at risk of mucormycosis. The study protocol was registered on the PROSPERO database (CRD42023478667). Findings: quantitative) had less impact on heterogeneity. Interpretation: This meta-analysis confirms the high performance of PCR for diagnosing mucormycosis and supports the instatement of PCR detection of free-DNA in blood, BALF and tissue into future updated definitions and diagnostic guidelines for mucormycosis. Funding: None.
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".