Preoperative prognostic factors associated with postoperative delirium in older people undergoing surgery: protocol for a systematic review and individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND: Early identification of patients at risk for postoperative delirium is essential because adequate well-timed interventions could reduce the occurrence of delirium and the related detrimental outcomes. METHODS: We will conduct a systematic review and individual patient data (IPD) meta-analysis of prognostic studies evaluating the predictive value of risk factors associated with an increased risk of postoperative delirium in elderly patients undergoing elective surgery. We will identify eligible studies through systematic search of MEDLINE, EMBASE, and CINAHL from their inception to May 2020. Eligible studies will enroll older adults (≥ 50 years) undergoing elective surgery and assess pre-operative prognostic risk factors for delirium and incidence of delirium measured by a trained individual using a validated delirium assessment tool. Pairs of reviewers will, independently and in duplicate, screen titles and abstracts of identified citations, review the full texts of potentially eligible studies. We will contact chief investigators of eligible studies requesting to share the IPD to a secured repository. We will use one-stage approach for IPD meta-analysis and will assess certainty of evidence using the GRADE approach. DISCUSSION: Since we are using existing anonymized data, ethical approval is not required for this study. Our results can be used to guide clinical decisions about the most efficient way to prevent postoperative delirium in elderly patients. SYSTEMATIC REVIEW REGISTRATION: CRD42020171366 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.044 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".