Stress hyperglycaemia in critically ill patients and the subsequent risk of diabetes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hyperglycaemia occurs frequently in critically ill patients without diabetes. We conducted a systematic review and meta-analysis to evaluate whether this 'stress hyperglycaemia' identifies survivors of critical illness at increased risk of subsequently developing diabetes. METHODS: We searched the MEDLINE and Embase databases from their inception to February 2016. We included observational studies evaluating adults admitted to the intensive care unit (ICU) who developed stress hyperglycaemia if the researchers reported incident diabetes or prediabetes diagnosed ≥3 months after hospital discharge. Two reviewers independently screened the titles and abstracts of identified studies and evaluated the full text of relevant studies. Data were extracted using pre-defined data fields, and risk of bias was assessed using the Newcastle-Ottawa Scale. Pooled ORs with 95 % CIs for the occurrence of diabetes were calculated using a random-effects model. RESULTS: = 36.5 %). Studies differed with regard to definitions of stress hyperglycaemia, follow-up and cohorts studied. CONCLUSIONS: Stress hyperglycaemia during ICU admission is associated with increased risk of incident diabetes. The strength of this association remains uncertain because of statistical and clinical heterogeneity among the included studies.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| 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".