Spectrum of Microbial Etiology of Community‐Acquired Pneumonia in Hospitalized Patients: Implications for Selection of the Population for Enrollment in Clinical Trials
Bibliographic record
Abstract
The title of this article implies that knowledge of the etiological pathogen may be useful in selection of patients for clinical trials for community-acquired pneumonia (CAP). However, this remains to be seen. The clinical course of a patient with CAP admitted to the hospital but not to the intensive care unit depends on a number of variables, including the patient, the pathogen, and the hospital itself. The site-of-care decision can be based on 1 of 2 prediction rules. Neither of these rules, however, correlates with the etiology of CAP, and it is not clear whether they can be used to stratify patients according to prognostic factors. A pathogen may be found in only approximately one-third of hospitalized patients with CAP overall. An etiological diagnosis is more likely to be made for patients with CAP who are hospitalized in the intensive care unit (39%) than for those hospitalized in other wards (20%). The issue of randomization to treatment regimens and possible approaches to randomization are discussed. It seems clear, however, that randomization would have to take place immediately after entry of the patient into the study. The possibility of using risks for specific pathogens or risks for antimicrobial resistance is also addressed. However, there are no data to support the use of such risks as prognostic factors in CAP. The best approach for noninferiority trials involving hospitalized patients with CAP is to randomize patients who meet the inclusion criteria and to stratify them by hospital site, with block randomization within each site. Stratification by site takes into account local epidemiology and can balance differences in unmeasured confounders among sites.
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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 | 0.192 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".