Optimizing use of the (1-3)-β-D-glucan assay for the diagnosis of Pneumocystis jirovecii pneumonia
Bibliographic record
Abstract
Objectives: Pneumocystis jirovecii pneumonia (PCP) can be challenging to diagnose because the tests used are imperfect and/or invasive.The Fungitell serum (1-3)-β-D-glucan (BDG) assay is one non-invasive test; however, it only has moderate sensitivity and specificity.Selecting which patients to test and subsequently correctly interpreting the results is essential.Methods: We previously proposed a clinical prediction rule to estimate the probability of proven or probable PCP.Herein, we combine this rule with a two-level interpretation of the BDG supported by a recent diagnostic test meta-analysis to identify patients for whom a negative BDG would have a negative predictive value (NPV) above 95%, those for whom a positive BDG would have a positive predictive value (PPV) closest to 95%, and those unlikely to benefit from BDG testing.Results: A negative BDG (<80 pg/mL) with a PCP score of ≤4 in HIV or ≤3 without HIV yields a NPV ≥95%.Above these scores, the post-test probability of a negative BDG exceeds 5%.With a positive BDG above 400 pg/ mL, a score ≥5.5 in HIV and ≥6.5 without HIV would be required to have a PPV between 90 and 95%.Between these ranges, a BDG can neither exclude nor rule in the diagnosis and thus would have low diagnostic value. Conclusions:We propose an approach based on pretest probability for employing and interpreting the BDG assay in the evaluation of patients with suspected PCP.The clinical prediction rule and diagnostic approach should be prospectively validated in a multicentre study.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".