Pneumonia Severity Index in the Immunocompromised
Bibliographic record
Abstract
BACKGROUND: The pneumonia severity index (PSI) accounts for many comorbidities, but not immunosuppression. OBJECTIVES: To document the utility of the PSI to predict mortality in immunocompromised patients (IP) with community-acquired pneumonia (CAP). METHODS: Charts of 284 patients with immunosuppression and CAP were reviewed, and these patients were compared with a contemporary sample of non-IP with CAP. The ability of the PSI to predict mortality was assessed by using multiple logistic regression. Discrimination of the PSI was studied by using the concordance index. RESULTS: Thirty-nine of 284 IP died. Mortality varied according to the etiology of the immunosuppression. Patients with HIV, solid organ transplantation or treatment with immunosuppressive drugs (n=118) had a low in-hospital mortality (4.3%) and were classified as low risk. IP with hematological malignancies, chemotherapy, chest radiation or marrow transplantation (n=166) had a high mortality (20%) and were classified as high risk. Compared with non-IP, low-risk IP had similar PSI-controlled mortality (OR=0.9, P=0.80), whereas high-risk IP had significantly greater mortality (OR=2.8, P<0.0001). The concordance index revealed similar discrimination for the PSI in low-risk IP (0.77) and in non-IP (0.7), but inferior discrimination in high-risk patients (0.6). CONCLUSIONS: Patients with CAP and immunosuppression can be divided into low-risk and high-risk groups. The low-risk IP have mortality similar to non-IP and can be risk stratified by using the PSI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".