Epidemiology, Risk Factors, and Prophylaxis Use for <i>Pneumocystis jirovecii</i> Pneumonia in the Non-HIV Population: A Retrospective Study in Québec, Canada
Bibliographic record
Abstract
Background: pneumonia (PJP) remains a significant threat in immunocompromised cases. Recent data on epidemiology and risk factors for PJP in non-HIV cases are scarce, and guidelines on appropriate prophylaxis are lacking. Methods: In this multicenter retrospective trial, all non-HIV adult cases admitted to hospitals in Québec City, Canada, between January 2011 and January 2021 with a diagnosis of PJP were assessed for eligibility. Results: An overall 129 cases of PJP were included. More than two-thirds had an underlying hematologic disease or an autoimmune/inflammatory condition. Prior to diagnosis, 83.7% were taking corticosteroids, 71.3% immunosuppressive agents (alone or in combination with corticosteroids), and 62% both. A diagnosis of PJP was noted in 22 patients receiving corticosteroids for treatment <28 days. Two patients developed PJP while undergoing corticosteroid monotherapy at a mean daily prednisone-equivalent dose <20 mg/d; 4.7% of our cohort received a PJP prophylaxis. Current recommendations or accepted clinical practices for PJP prophylaxis would not have applied to 48.8% of our patients. Conclusions: The use of corticosteroids-in monotherapy or in coadministration with other immunosuppressive agents-remains the principal risk factor for PJP in the non-HIV population. Current prophylaxis guidelines and accepted practices are insufficient to adequately prevent PJP and need to be broadened and updated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".