Additional file 1 of Adverse effects of delayed antimicrobial treatment and surgical source control in adults with sepsis: results of a planned secondary analysis of a cluster-randomized controlled trial
Bibliographic record
Abstract
Additional file 1. Supplemental Figures. SFigure 1. Calibration belt of the risk-adjustment-model for 28-day-mortality. The figure shows observed 28-day mortality (y-axis) compared to model based estimates of expected 28-day mortality (x-axis). SFigure 2. Flow-diagram of the study. SFigure 3. Panel a: Timing of start of antimicrobial treatment in relation to onset of sepsis. Panel b: Timing of surgical source control in relation to onset of sepsis. Panel c: Cumulative proportion of patients receiving antimicrobial treatment after onset of sepsis within the first 48 hours. Panel d: Cumulative proportion of patients receiving surgical source control after onset of sepsis within the first 48 hours. SFigure 4. Crude mortality and predicted mortality depending on success of surgical source control. Presented are the risk (bars) and predicted risk (lines) across a range of time after onset of sepsis. Bold lines present the predicted risk with 95% confidence interval for a typical patient from a model adjusting for covariates, dotted lines present the predicted risk with 95% confidence interval from a model without adjusting for covariates. The effect of timing was tested for linearity by fractional polynomials at significance level 0.05; timing was treated as linear, since no significant deviation from linearity was found. Risk-adjusted OR with 95% CI: 0.991 [0.978, 1.005], p = 0.197; raw OR with 95% CI: 0.982 [0.971, 0.994], p = 0.003. N = 1595 cases did undergo surgical source control within 48 hours, of which 1592 cases had complete data on outcome and covariates for analysis. The risk-adjustment model involved the following covariates: age and gender, origin of infection, location of the patient at the onset of sepsis, focus of infection, microbiological confirmation of infection, study phase (trial vs. surveillance phase), and group the hospital was randomized to (intervention vs. control).
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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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.793 | 0.035 |
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".