New antipsychotic prescription and recurrent infections among adult sepsis survivors: A population‐based cohort study
Bibliographic record
Abstract
Abstract Purpose Antipsychotic agents, which may increase the risk of infection through dopaminergic dysregulation, are prescribed to a fraction of patients following critical illness. We compared the rate of recurrent sepsis among patients who filled a prescription for antipsychotics with high‐ or low‐D2 affinity. Methods Population‐based cohort with active comparator design. We included sepsis survivors older than 65 years with intensive care unit admission and new prescription of antipsychotics in Ontario 2008–2019. The primary outcome were recurrent sepsis episodes within 1 year of follow‐up. Patients who filled a prescription within 30 days of hospital discharge for high‐D2 affinity antipsychotics (e.g., haloperidol) were compared with patients who filled a prescription within 30 days of hospital discharge for low‐D2 affinity antipsychotics (e.g., quetiapine). Multivariable zero‐inflated Poisson regression models with robust standard errors adjusting for confounding at baseline were used to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI). Results Overall, 1879 patients filled a prescription for a high‐D2, and 1446 patients filled a prescription for a low‐D2 affinity antipsychotic. Patients who filled a prescription for a high‐D2 affinity antipsychotic did not present a higher rate of recurrent sepsis during 1 year of follow‐up, compared with patients who filled a prescription for a low‐D2 affinity antipsychotic (IRR: 1.12; 95% CI: 0.94, 1.35). Conclusions We did not find conclusive evidence of a higher rate of recurrent sepsis associated with the prescription of high‐D2 affinity antipsychotics (compared with low‐D2 affinity antipsychotics) by 1 year of follow‐up in adult sepsis survivors with intensive care unit admission.
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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 | 0.000 |
| 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".