Additional file 1 of Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort
Bibliographic record
Abstract
Additional file 1. Methods: Detailed description of statistical analysis. Fig. S1: Heatmap of correlation between selected variables for phenotyping. Fig. S2: Consensus k clustering results. Fig. S3: Comparison of host response biomarkers levels at ICU discharge between subtypes across different subgroups of Charlson age–comorbidity index terciles. Fig. S4: Comparison of host response biomarkers levels at ICU discharge between subtypes across different subgroups of at ICU discharge SOFA terciles. Fig. S5: Comparison of host response biomarkers levels at ICU discharge between subtypes across different subgroups of on admission SAPS II terciles. Fig. S6: Comparison of host response biomarkers levels at ICU discharge between subtypes across different subgroups of sepsis severity at inclusion. Table S1: Selected variables included in the LCA model. Table S2: Cardiovascular, inflammatory and renal biomarkers measured at ICU discharge. Table S3: Clinical and biological variables at ICU discharge based on subtypes. Table S4: Site of infection and microbiological differences between subtypes. Table S5: Comparison of LCA models at discharge with different numbers of classes in a representative imputed dataset. Table S6: Patients Characteristics’ according to one-year mortality after ICU discharge. Table S7: Cox proportional hazards models to adjust for confounding (age, chronic kidney disease, diabetes mellitus, duration of ICU stay, SAPS II on admission, SOFA score at ICU discharge) for one-year mortality. Table S8: Cox proportional hazards models to adjust for confounding (age, chronic kidney disease, diabetes mellitus, duration of ICU stay, SAPS II on admission, SOFA score at ICU discharge) for one-year mortality. Table S9: Initial and reduced biomarker regression models to discriminate the two subtypes at ICU discharge. Table S10: Characteristics of the main clinical studies using an unsupervised approach (i.e., phenotyping) to identify different classes in sepsis-survivors after ICU discharge.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.757 | 0.084 |
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".