Pneumococcal Peritonitis: Still with Us and Likely to Increase in Importance
Bibliographic record
Abstract
BACKGROUND: Pneumococcal peritonitis is uncommon and poorly understood. METHODS: As part of a five-year study (2000 to 2004) of invasive pneumococcal disease (IPD) in Alberta, all cases of peritonitis due to Streptococcus pneumoniae were reviewed and compared with all other cases of IPD. RESULTS: Twenty-three of 1768 (1.3%) IPD patients were found to have peritonitis. Patients with peritonitis were more likely to have cirrhosis, hepatitis C, alcoholism and HIV/AIDS, than the remainder of the patients with IPD. The all-cause mortality did not differ between the two groups. Peritonitis was classified as primary in nine (39%) patients, secondary in 12 (52%) patients, and genitourinary in females, specifically, in two (9%) patients. Pneumococcal serotypes causing peritonitis were under-represented in current vaccines - 17% among peritonitis patients versus 53% for the remainder of IPD patients for the 7-valent pneumococcal conjugate vaccine, and 56% versus 86% for the 23-valent pneumococcal polysaccharide vaccine. CONCLUSIONS: Peritonitis represents a small subset of patients with IPD, but one that is likely to grow in importance given the increase in the number of patients with hepatitis C and HIV, and the reduced coverage of peritonitis serotypes in currently available vaccines.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".