Risk Factors for the Development of Post-Operative Cognitive Dysfunction
Bibliographic record
Abstract
BACKGROUND: Many studies have shown that a large number of patients undergoing surgery show a measurable cognitive deterioration after surgery, while many of them still show cognitive deficits even three months later an operation. These specific cognitive deficits in which there is a temporal association between surgery and mental disorders are defined as postoperative impairment of cognitive function. Among cognitive disorders occurring during the postoperative period, the post-operative cognitive dysfunction (POCD) is less studied.AIM: Risk factors concerning POCD will be overviewed in order to be considered as a measure of prevention of POCD.METHOD: A literature search using combined keywords was undertaken on bibliographic databases including PubMed, Google Scholar and Scopus and through systematic selection 72 scientific articles were identified. Concerning the selection criteria, the material of this study consists of sources published mainly over the last fifteen years, while some articles that published before 2000 were selected because they were considered to be important.RESULTS: These disorders frequently occur in patients of advanced age. It is obvious that as the population of humanity ages, many older people are likely to develop health problems that require surgery and therefore a large number of people are likely to develop post-operative cognitive disorders. For the appearance of POCD, as for other mental disorders (e.g. delirium), several factors are implicated. According to the findings, except the advanced age, genetic polymorphism, idiosyncratic condition, the presence of metabolic syndrome and neurological diseases, the type of anaesthesia and surgical operation and sleep disturbance are among the most important risk factors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".