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Record W4413932767 · doi:10.20471/acc.2024.63.03-04.33

The Effect of Anesthesia on Postoperative Cognitive Dysfunction

2024· article· en· W4413932767 on OpenAlexaboutno aff
Mario Bilić

Bibliographic record

VenueActa Clinica Croatica · 2024
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsnot available
Fundersnot available
KeywordsPostoperative cognitive dysfunctionAnesthesiaMedicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.360
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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