First-generation antipsychotics use and reduced risk of pneumonia—Clinical implications in SARS-CoV2 treatment: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
The use of antipsychotics (AP) has been linked to nearly 60% increase in the incidence of pneumonia. The study purposes to devise safest treatment regimens for psychiatric patients with underlying respiratory comorbidities. A systematic literature search was conducted. A total of 41 studies were evaluated, which included 33 articles for metaanalysis. The quality of retrieved articles was screened by reviewing independently. The risk of bias in each study was assessed using the Newcastle-Ottawa Scale. Inter-rater agreement calculation was performed using Rayyan QCRI. Statistical analysis was performed using R 4.0.3. The meta-analysis conducted revealed that the risk of pneumonia (OR = 1.66; 95% CI = 1.64-1.68) and respiratory failure (OR = 1.79; 95% CI = 1.61-2.00) were higher in psychotropic users compared to nonusers. Pneumonia risk was higher in second-generation antipsychotic users (OR = 1.12; 95% CI = 1.01-1.25) compared to other antipsychotic users. However, no association was found between firstgeneration antipsychotics and pneumonia compared to other psychotropic exposure (OR = 0.93; 95% CI = 0.86-0.99). Chlorpromazine, sulpiride, and aripiprazole were found to be statistically safer compared to other AP. AP should be of appropriate choice in patients with SARS-CoV-2 infection, recurrent pneumonia history or those with opportunistic infections.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".