Additional file 1 of Attributable mortality due to nosocomial sepsis in Brazilian hospitals: a case–control study
Bibliographic record
Abstract
Additional file 1: Table S1. Hospital Features. Table S2. Comparison between patients that develop and did not develop nosocomial sepsis according to the main definition. Table S3. Sepsis episodes. Figure S1. Venn diagram for the sepsis definitions used in the manuscript. Figure S2. Daily patient location/status over time for controls (top) and cases (bottom). Figure S3. Number of nosocomial sepsis episodes according to infection source. Figure S4. Number of organ dysfunctions at presentation for (A) first episode of sepsis, and (B) all sepsis episodes. Figure S5. Patient locale at sepsis diagnosis. Top: Stratified according to outcome; Bottom: All patients. Table S4. Pathogens isolated from cultures according to suspected site; more than one pathogen was possible for each patient. CRBI: Catheter related bloodstream infections. Note that pathogens could be isolated in any culture collected from the patient within the 48h timeframe. The local diagnosis was considered as reference; therefore the isolated pathogen could not be considered the culprit for the infection. Table S5. Positive cultures for patients that had one septic episode according to main definition. CRBI: Catheter related bloodstream infections. Note that pathogens could be isolated in any culture collected from the patient within the 48h timeframe. Same as in Table S3, the final diagnosis was made by the site. Figure S6. (A) Infection source considering first alternative definition. The number of patients that did not receive antibiotic reflects patients that developed new organ failure in the absence of any other clinically relevant event and were considered as possibly septic under this definition. (B) Organ dysfunction at presentation for the alternative definition 1 analysis. Figure S7. Distribution of odds ratio (upper row) and AF (lower row) according to admission type (columns) for the first alternative definition considering the effects of definitive, very probably, and probable sepsis. Figure S8. Sepsis sources (A) and organ dysfunction at presentation for the second alternative definition analysis. Figure S9. Distribution of odds ratio (upper row) and PAF (lower row) according to admission type (columns) for the second alternative definition considering the effects of definitive and very probably sepsis. Figure S10. Distribution of odds ratio (upper row) and PAF (lower row) according to admission type (columns) for the post hoc definition based on SOFA score. Figure S11. Distribution of odds ratio (upper row) and PAF (lower row) according to admission type (columns) after excluding patients with infection at baseline. Figure S12. Comparison of the three definitions for elective surgery admissions. Upper panel: The daily odds ratio for mortality in elective surgery patients according to the definition used. Bottom panel: The respective AF for each definition. Note that a higher OR does not equal higher AF since prevalence of events also changes. Figure S13. Comparison of the three definitions for emergency surgery admissions. Upper panel: The daily odds ratio for mortality in medical patients according to the definition used. Bottom panel: The respective AF for each definition. Note that a higher OR does not equal higher AF since prevalence of events also changes.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.483 | 0.013 |
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".