Frequency of postoperative cognitive dysfunction after non-cardiac surgery and its impact on functional outcomes: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Older surgical candidates are at increased risk of a phenomenon known as postoperative cognitive dysfunction (POCD). Several studies have looked at the incidence of POCD at different time points following surgery, using different study methods. Fewer have assessed whether changes in cognition after surgery are attributable to surgery and how they impact patient function and quality of life. The aim of this systematic review is to summarise and appraise studies addressing any of the following research questions (RQs): (RQ1) what is the frequency of POCD after non-cardiac surgery?; (RQ2) is non-cardiac surgery associated with an increased risk of cognitive decline?; (RQ3) is POCD after non-cardiac surgery associated with patient-important outcomes? METHODS AND ANALYSIS: This protocol adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines. Three electronic databases (MEDLINE, PsycINFO and EMBASE) will be systematically searched from their inception date. Identified studies will be screened by two reviewers for eligibility using Covidence, and data will be extracted into a standardised electronic form. We will evaluate methodological quality of included studies using the Quality In Prognosis Studies and its adaptation to the overall prognosis question, and the CLARITY risk of bias for cohort and case-control studies. For RQ1, we will estimate an average POCD frequency at different time points by performing a meta-analysis of included studies when appropriate. For RQ2 and RQ3, we will extract and meta-analyse the effect measures for the association of surgery with cognitive decline when compared with the non-surgical comparator, and association of cognitive changes with functional changes, quality of life and other patient-important outcomes based on available evidence. We will narratively summarise and discuss the different methods implemented in the existing studies to answer the three RQs, and when meta-analysis is deemed infeasible, we will qualitatively report the results of the included studies. ETHICS AND DISSEMINATION: This project involves the collection and analysis of data from previously published studies and therefore does not require ethics approval. We plan to present the findings of this research project at peer-reviewed conferences and publish the results in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42022370674.
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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 | 0.069 | 0.109 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.007 |
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".