Additional file 1 of Ground truth labels challenge the validity of sepsis consensus definitions in critical illness
Bibliographic record
Abstract
Additional file 1: Appendix S1. Questionnaire for the evaluation of SIRS and sepsis. Appendix S2. Agreement and performance of clinical criteria for Sepsis-1/2 and Sepsis-3 compared to GTSQ sepsis labels. Text S1. GTSQ construction and survey implementation. Text S2. Encounter definition. Text S3. Methods for evaluation of interrater agreement. Text S4. Features for SIRS and SOFA. Text S5. Supplementary results of interrater reliability study. Table S1. Contingency table for working diagnoses (Item 3) of interrater reliability study. Table S2. Krippendorff’s α values for questionnaire items of interrater reliability study. Table S3. Additional measures of agreement of questionnaire items in interrater reliability study. Table S4. GTSQs with labels for acute organ dysfunction (Item 9) by working diagnosis (Item 3). Table S5. Association of acute organ dysfunction (Item 9) with focus localization (Item 5). Table S6. Characteristics of complete encounters by working diagnosis (Item 3) in the subgroup analysis. Table S7. Responses to GTSQ items by working diagnosis label (Item 3) in the subgroup analysis. Fig. S1. Clinical characteristics for all edited GTSQs by working diagnosis (Item 3). Values of clinical characteristics in the 2 PM–2 PM-rating intervals for all 7.291 edited GTSQs (cf. Table 3 of the main text) were retrieved from the ICU’s PDMS. Mean values are displayed as box plots colored by working diagnosis (Item 3) as indicated in the legend.
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 | 0.009 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.847 | 0.161 |
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".