Association between diabetes mellitus and poor patient outcomes after out-of-hospital cardiac arrest: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Diabetes mellitus (DM) serves as an important prognostic indicator in patients with cardiac-related illness. Our objective is to compare survival and neurological outcomes among diabetic and non-diabetic patients who were admitted to the hospital after an out-of-hospital cardiac arrest (OHCA). We searched MEDLINE and EMBASE for relevant articles from database inception to July 2018 without any language restriction. Studies were included if they evaluated patients who presented with OHCA, included mortality and neurological outcome data separately for DM patients and Non-DM patients and reported crude data, odds ratio (OR), relative risk (RR) or hazard ratio (HR). Two investigators independently reviewed the retrieved citations and assessed eligibility. The quality of included studies was evaluated using Newcastle-Ottawa quality assessment scale for cohort studies. Random-effect models using the generic variance method were used to create pooled odds ratios (OR) and 95% confidence intervals (CI). Heterogeneity was assessed using the I 2 value. Survival and neurological outcomes (using modified rankin scale and cerebral performance category scale) after OHCA in hospitalized patients with DM compared with patients without DM. Out of 57 studies identified, six cohort studies met the inclusion criteria. In an analysis of unadjusted data, patients with DM had lower odds of survival, pooled OR 0.64; 95% CI, 0.52–0.78, [I 2 = 90%]. When adjusted ORs were pooled, the association between DM and survival after OHCA was still significantly reduced, pooled OR 0.78, 95% CI, 0.68–0.89 [I 2 = 55%]. Unadjusted pooled OR revealed poor neurological outcomes in patients with DM, pooled OR 0.55, 95% CI, 0.38–0.80 [I 2 = 90%]. The result demonstrates significant poor outcomes of in-hospital survival and neurological outcomes among DM patients after OHCA.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".