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Record W2972423201 · doi:10.1177/1750458918808163

Assessing the feasibility of using the Dalhousie Computerized Attention Battery to measure postoperative cognitive dysfunction in older patients

2018· article· en· W2972423201 on OpenAlexaffabout
Yaeesh Sardiwalla, Gail A. Eskes, André Bernard, Ronald B. George, Michael G. Schmidt

Bibliographic record

VenueJournal of Perioperative Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsPerioperativeCognitionCognitive testMedicinePhysical therapyMoodCognitive Assessment SystemPostoperative cognitive dysfunctionBattery (electricity)Montreal Cognitive AssessmentPsychologyClinical psychologyPsychiatryCognitive impairmentSurgery

Abstract

fetched live from OpenAlex

Purpose Postoperative cognitive dysfunction is difficult to predict and diagnose, and can have severe consequences in the long term. The purpose of this study was to examine the feasibility of using a computerised test battery, the Dalhousie Computerized Assessment Battery in the perioperative clinic to detect cognitive changes after surgery. Methods Fifty patients were recruited for this study. Patients completed the Dalhousie Computerized Assessment Battery and tests of general cognition, mood and pain at baseline and at three months postoperatively. Results This pilot study had a screening rate (85.4%) and low attrition rate (12%). At baseline, patients exhibited no significant cognitive differences compared to a normative dataset. Postoperative cognitive dysfunction incidence was 2.7% on Montreal Cognitive Assessment, 13.6% with Dalhousie Computerized Assessment Battery and 36.3% based on subjective reports. Conclusion Computerised cognitive testing in the perioperative setting proved feasible. Deficits in spatial working memory and dual tasks may be most compromised by surgically related variables.

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)0.002
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.063
GPT teacher head0.387
Teacher spread0.324 · 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.

Study designObservational
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

Citations2
Published2018
Admission routes2
Has abstractyes

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