Can we predict which patients with community-acquired pneumonia are likely to have positive blood cultures?
Bibliographic record
Abstract
BACKGROUND: Blood cultures (BC) are commonly ordered during the initial assessment of patients with community-acquired pneumonia (CAP), yet their yield remains low. Selective use of BC would allow the opportunity to save healthcare resources and avoid patient discomfort. The study was to determine what demographic and clinical factors predict a greater likelihood of a positive blood culture result in patients diagnosed with CAP. METHODS: A structured retrospective systematic chart audit was performed to compare relevant demographic and clinical details of patients admitted with CAP, in whom blood culture results were positive, with those of age, sex, and date-matched control patients in whom blood culture results were negative. RESULTS: On univariate analysis, eight variables were associated with a positive BC result. After logistic regression analysis, however, the only variables statistically significantly associated with a positive BC were WBC less than 4.5 × 10(9)/L [likelihood ratio (LR): 7.75, 95% CI=2.89-30.39], creatinine >106 μmol/L (LR: 3.15, 95%CI=1.71-5.80), serum glucose<6.1 mmol/L (LR: 2.46, 95%CI=1.14-5.32), and temperature > 38 °C (LR: 2.25, 95% CI =1.21-4.20). A patient with all of these variables had a LR of having a positive BC of 135.53 (95% CI=25.28-726.8) compared to patients with none of these variables. CONCLUSIONS: Certain clinical variables in patients with CAP admitted to hospitals do appear to be associated with a higher probability of a positive yield of BC, with combinations of these variables increasing this likelihood. We have identified a subgroup of CAP patients in whom blood cultures are more likely to be useful.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".