Additional file 1 of Persistent hyperglycemia is a useful glycemic pattern to predict stroke mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Supplemental file 1. Search strategy. Table S1. Quality evaluation of included studies using the Newcastle-Ottawa Quality Assessment Scale (cohort study). Table S1. Hou et al., 2021. Baseline characteristics of the study population and bivariate comparisons between patients with favorable and unfavorable outcomes. Table S2. Hou et al., 2021. Comparisons of short-term outcomes between patients with and without persistent hyperglycemia. Table S3. Hou et al., 2021. Baseline characteristics of the study population and bivariate comparisons between short-duration and long-duration persistent hyperglycemia groups. Table S4. Hou et al., 2021. Comparisons of short-term outcomes between patients with short- and long-duration persistent hyperglycemia. Table S5. Hou et al., 2021. Comparisons of short-term outcomes between persistent hyperglycemia patients with HbA1c < 7% and HbA1c > = 7%. Figure S1. Funnel plots of PH, AH, SH, non-PN for predicting post-stroke mortality and post-stroke hemorrhage [from upper left to upper right (A–B), middle left to middle right (C–D, E–F), lower left to lower right (G–H)]. Figure S2. egger: Egger’s test results of PH and non-PH groups in the general population, PH and non-PH groups without DM, AH and PH groups without DM, and AH and PN groups without DM for predicting post-stroke mortality. Figure S3. Forest plot of PH and non-PH for predicting mortality in patients with ischemic stroke. Figure S4. Revised overall comparisons between PH and non-PH, AH and non-AH, SH and non-SH, PN and non-PN groups (revised figure 2A, removed some data that may cause heterogeneity).
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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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.010 |
| 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) | 0.677 | 0.023 |
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".