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Record W4385957260 · doi:10.4103/sja.sja_529_23

Postoperative cognitive recovery and prevention of postoperative cognitive complications in the elderly patient

2023· review· en· W4385957260 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSaudi Journal of Anaesthesia · 2023
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicinePerioperativePostoperative cognitive dysfunctionDeliriumCognitionCognitive impairmentIntensive care medicineCognitive declineInformed consentPopulationSurgeryDementiaPsychiatryDiseaseAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

Elderly patients undergoing surgery are at higher risk of life-altering and costly complications. This challenge is increasingly recognized with the growing geriatric surgical population. Advanced age and comorbid conditions, such as disability and frailty that often develop with age, are all independent risk factors of postoperative morbidity and mortality. A common factor in this age group is cognitive impairment, which poses a challenge for the patient and clinician in the perioperative setting. It affects the capacity for informed consent and limits optimization before surgery; furthermore, an existing impairment may progress in severity during the perioperative period, and new onset of signs of delirium or postoperative cognitive dysfunction may arise during postoperative recovery. In this article, we aim to review the current literature examining the latest definitions, diagnostic criteria, and preventive strategies that may ameliorate postoperative cognitive complications.

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.353
Teacher spread0.301 · 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