Duration of Pneumocystis jirovecii pneumonia prophylaxis in adult kidney transplant recipients and outcomes: A population-based study
Bibliographic record
Abstract
Kidney transplant recipients are prescribed medications to prevent Pneumocystis jirovecii pneumonia (PJP), but there is no consensus regarding the optimal duration of therapy. We used linked healthcare databases in Alberta, Canada to describe patterns of PJP prophylactic prescriptions in incident adult kidney transplant recipients (2008-2019) to compare clinical outcomes in recipients taking short-term (≤9 months) versus long-term (>9 months) PJP prophylaxis. We identified 1,265 kidney transplant recipients. The median age was 53 years (IQR 41-62), 35% were female, and the index eGFR was 59 ml/min/1.73 m 2 (IQR 47-72). There were 448 (35%) recipients on long-term PJP prophylaxis and 817 (65%) recipients on short-term (median follow-up: 5.7 vs. 4.8 years, respectively). There was no significant difference in the risk of all-cause mortality between the groups (11% vs. 13%). Recipients in the long-term group were 46% less likely to experience graft failure (4% vs. 7%). The risk of hospitalization for PJP infection was low (n=3, 0.2%), with no significant difference between the groups. Compared to short-term PJP prophylaxis, long-term PJP prophylaxis did not significantly reduce the risk of post-transplant mortality or infection, including PJP infection. Further research is needed to explore the association between PJP prophylaxis and graft survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".