Additional file 1 of The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
Bibliographic record
Abstract
Additional file 1: Figure S1. Clinical Frailty Scale. Permission to use this scale was granted from Dalhousie University, Ca, May 15 2017. Figure S2. Flow chart showing the collection, conversion, extraction, integration, and control of the individual patient data. Figure S3. Overview on the statistical approach. Table S1. Inclusion and exclusion criteria for studies and patients, respectively. Table S2. Overview about the studies that contributed data – Part 1. Table S3. Overview about the studies that contributed data – Part 2. Table S4. Reported and collected data for each study. Table S5. Quality rating for the risk of bias using QUIPS. Table S6. aHRfor being frail. Table S7. aHRfor being vulnerable. Table S8. Regression analyses for ICU mortality, adjusted to APACHE II or SAPS II. Table S9. Overview on the different ICU-scores SOFA; SAPS II and APACHE II. Table S10. Origin countries of the included data sets.
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 | 0.005 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.790 | 0.039 |
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".