Severe Community‐Acquired Pneumonia (CAP) and the Infectious Diseases Society of America/American Thoracic Society CAP Guidelines Prediction Rule: Validated or Not
Bibliographic record
Abstract
Identifying patients with severe community-acquired pneumonia (CAP) who require admission to an intensive care unit (ICU) can, at times, be a difficult and daunting task. It is not always clear which patients will benefit from the additional diagnostic, treatment, and management protocols and procedures of the ICU, and the consequences of a poor selection process can be disastrous. ICU facilities, resources, and personnel are relatively limited in most hospitals. Therefore, the inappropriate admission to the ICU of patients with CAP who do not require such care may prevent a patient who does require such care from accessing it. The subsequent transfer of patients with CAP who are first admitted to a hospital ward to the ICU for delayed onset of respiratory failure or septic shock is associated with increased mortality [1]. To anyone who cares for patients who may have severe CAP, it is obvious that the course of the disease is dynamic and that neither clinical nor laboratory values remain static. It can be difficult to differentiate between individuals who require ICU care at the time of assessment in the emergency department and those whose conditions will worsen after admission to the hospital. Ideally, we would like to identify patients who require ICU care as early as possible. Having an accurate prediction rule that allows physicians to select patients with severe CAP who require ICU treatment early in the course of illness facilitates the appropriate initial management and antibiotic treatment and is an important strategy for mortality reduction [2].
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".