Additional file 1 of Prevalence and prognostic value of preexisting sarcopenia in patients with mechanical ventilation: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Table S1. PRISMA 2020 Checklist. Table S2. Search strategy by MEDLINE, Embase, The Cochrane Database of Systematic Reviews, and The Cochrane Central Register of Controlled Trials via Ovid SP. Table S3. The impact of sarcopenia on mortality in patients with MV. Table S4. The reasons for the exclusion of full-text articles. Table S5. The details of diagnosis criteria and cutoff points of each study. Table S6. Result of the Newcastle–Ottawa scale quality assessment. Fig. S1. Subgroup analysis of sarcopenia prevalence at different CT sites. Fig. S2. Meta-regression of the effect of average age on sarcopenia prevalence. Fig. S3. Meta-regression of the effect of average age on mortality. Fig. S4. The duration of mechanical ventilation. Fig. S5. The length of ICU stay. Fig. S6. The length of hospital stay. Fig. S7. Subgroup analysis of effects with different diagnostic methods on the duration of mechanical ventilation. Fig. S8. Subgroup analysis of effects with different diagnostic methods on the length of hospital stay. Fig. S9. The sensitivity analysis of prevalence. Fig. S10. The sensitivity analysis for ORs between sarcopenia and mortality. Fig. S11. Begg's and Egger's tests for publication bias of prevalence. Fig. S12. Begg's and Egger's tests for publication bias of mortality.
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 | 0.007 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.769 | 0.033 |
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".