Additional file 1 of Sepsis in burn care: incidence and outcomes
Bibliographic record
Abstract
Additional file 1. Fig. S1 Study flow diagram showing patient inclusion and exclusion criteria. Fig. S2 Differences in survival outcomes among adult burn patients with sepsis, stratified by Gram stain classification of the pathogen identified at diagnosis. Fig. S3 Differences in survival outcomes among older adult burn patients with sepsis, stratified by Gram stain classification of the pathogen identified at diagnosis. Table S1 Demographics and injury characteristics of adult sepsis patients based on infectious pathogen classification. Table S2 Univariate logistic regression analyses in adult burn patients examining the association between various independent variables and sepsis diagnosis. Table S3 Univariate logistic regression analyses examining the association between various independent variables and mortality in adult burn patients diagnosed with sepsis. Table S4 Demographics and injury characteristics of older adult sepsis patients based on infectious pathogen classification. Table S5 Univariate logistic regression analyses examining the association between various independent variables and sepsis diagnosis in older adult burn patients. Table S6 Univariate logistic regression analyses examining the association between various independent variables and mortality in older adult burn patients diagnosed with sepsis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.812 | 0.087 |
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".