Bibliographic record
Abstract
Postoperative cognitive dysfunction (POCD) is a newly developed cognitive function deficit after surgery. The aim of the study was to determine the incidence of POCD after anesthesia and possible risk factors. A prospective study was conducted on 90 patients scheduled for elective surgery under general (60 patients) or regional (30 patients) anesthesia. Each patient completed the Montreal cognitive assessment (MoCa) test the day before and the day after surgery. Data on comorbidities, previous COVID-19 infection, demographic and anesthesia related data were also collected. The day after surgery, POCD defined according to the 2 scores rule was present in 38 (42.2%) patients. A lower level of education (p=0.023), previous COVID-19 infection (p=0.032), higher Charlson comorbidity index (CCI) (p=0.014), and general anesthesia (p=0.035) were identified as risk factors, whereas a statistically significant negative correlation with preoperative (p=0.001) and postoperative result (p=0.001) was proven for age. The results indicated that a significant proportion of patients after general or regional anesthesia developed POCD depending on patient education, CCI, COVID-19 infection, and type of anesthesia. It was also shown that older age correlated with poorer MoCa test result independently of anesthesia. These factors can be identified before the procedure under anesthesia, thus offering the possibility of adjusting anesthesia and postoperative care in patients at risk of developing POCD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".