Additional file 1 of Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models
Bibliographic record
Abstract
Additional file 1: Supplementary Table 1. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) Checklist for Prediction model development. Supplementary Table 2. Algorithms of ICD-9 and 10 codes used to define components of our composite outcome. Supplementary Table 3. Candidate Predictor definition along with source of data and ICD-9/10 algorithms if applicable. Supplementary Table 4. Surgical Categories by Canadian Classification of Health Intervention (CCI) codes. Supplementary Table 5. Estimated Sample Size Calculations using ‘pmsampsize’ in Stata software v17.0 and as suggested by Riley et al (2020). Supplementary Table 6. Top causes of death for those that died within 30 days of surgery, with associated ICD-10 codes. Supplementary Table 7. Performance of models evaluated in cohort with only first surgery per participant. Supplementary Table 8. Event and non-eventReclassification Tables between models, stratified by clinically important probability categories. Supplementary Figure 1. Decision Curve Analysis to estimate the net benefit of use of perioperative risk prediction models in ambulatory or inpatient elective surgery (sensitivity analysis).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.064 | 0.000 |
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 teacher head, 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".