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Record W4401834964 · doi:10.1093/cid/ciae430

Reply to Hao and He

2024· article· en· W4401834964 on OpenAlexaff
Lottie Brown, Mario Cruciani, J. Peter Donnelly, Riina Rautemaa‐Richardson, P. Lewis White

Bibliographic record

VenueClinical Infectious Diseases · 2024
Typearticle
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsLibrary scienceMedicineHistoryFamily medicineComputer science

Abstract

fetched live from OpenAlex

To the Editor—We thank Drs Hao and He for their interest in our meta-analysis on the diagnosis of Pneumocystis pneumonia (PcP) by polymerase chain reaction (PCR). We agree that initiation of PcP treatment and underlying conditions likely have an impact on fungal burden and, subsequently, PCR performance. Our meta-analysis found no significant difference in PCR performance according to human immunodeficiency (HIV) status. Unfortunately, it was not possible to perform a stratified analysis according to other underlying conditions, patient demographics, or medication status as the studies included seldom presented sufficient data for comparative analysis according to these factors. We acknowledge inconsistent reporting as a limitation in our discussion, which should ideally be addressed in future studies. The impact of the anti-Pneumocystis therapy, be it prophylaxis, empirical, or targeted therapy, should be considered. The impact of anti-Pneumocystis prophylaxis on PCR performance is understudied, although this may be less relevant given that rates of PcP in patients on sulfamethoxazole-trimethoprim prophylaxis are low (<1%) [1]. Molecular tests have demonstrated the ability to detect resistant/breakthrough infections, indicating that the disease burden is sufficient to be detected by PCR [2]. The effect of empirical treatment on PCR positivity has not been fully investigated, particularly in HIV-negative patients in whom fungal burdens are lower and impact may be greater. Every effort should be made to obtain a respiratory specimen for PCR before starting treatment. Bronchoscopy may be delayed due to the need for specialist equipment and expertise so more readily available specimens, such as induced sputum and upper respiratory tract samples, are useful samples for combining with serum (1,3)-β-d-glucan (BDG). Similarly, the impact of targeted antifungal therapy on PCR performance has not been fully elucidated, although persisting PCR positivity is a poor prognostic marker in PcP and burden typically reduces within 10 days of successful therapy [3, 4]. There is a clear need for further studies evaluating the diagnostic accuracy of standardized PCR methods against the reference standard, with comparative performance according to underlying disease, use of prophylaxis, and timing of treatment initiation. We believe our methods are in line with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines and additional information is available in the supplementary material. When formatting our search terms, we considered the Population, Intervention, Comparator, Outcome, Study design (PICOS) principle to the extent that it is applicable to noninterventional diagnostic studies. In our systematic review, we did not provide a formal GRADE (Grading of Recommendations Assessment, Development and Evaluation) assessment of the certainty of the evidence because it is primarily designed for systematic reviews on interventional studies. Detailed guidance for rating certainty in comparative diagnostic accuracy reviews is still under development [5]. The studies included were of high methodological quality according to the QUADAS-2 tool, but the certainty of the evidence could only be classified as moderate due to the absence of proven infection, the most typical classification when managing respiratory fungal diseases. There are substantial challenges to achieving global standardization of PcP PCR. Our meta-analysis identified significant variations in PCR methodology across included studies, which will affect the concentration of fungal DNA and diagnostic yield. We also discuss specimen quality and operator skill as a source of heterogeneity that is difficult to quantify. Methodological standardization is being undertaken by the Fungal PCR Initiative (a working group of the International Society of Human and Animal Mycology (ISHAM) and strategies involving external quality-control organizations are being developed to provide an international standard for PcP PCR, potentially allowing it to become the reference test for PcP [6, 7]. We encourage manufacturers to obtain universal approval of their products to enable global availability as the World Health Organization already lists PcP PCR as an essential diagnostic test. Finally, we agree that PCR results should be combined with biomarkers and clinical and radiological findings and our group is exploring diagnostic algorithms that combine all available evidence to enhance diagnosis beyond probable PcP. Financial support. This work was co-funded by the National Institute for Health and Care Research and Manchester Biomedical Research Centre (NIHR203308).

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.107
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.107
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0050.006
Open science0.0040.003
Research integrity0.0190.031
Insufficient payload (model declined to judge)0.0130.008

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.

Opus teacher head0.031
GPT teacher head0.379
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations2
Published2024
Admission routes1
Has abstractyes

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