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Record W2029562704 · doi:10.1016/j.hkjot.2011.05.004

Cognitive Outcomes and Activity of Daily Living for Neurosurgical Patients with Intrinsic Brain Lesions: A 1-year Prevalence Study

2011· article· en· W2029562704 on OpenAlexaboutno aff
George Kwok Chu Wong, Rosanna Wong, Wai Sang Poon

Bibliographic record

VenueHong Kong Journal of Occupational Therapy · 2011
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsRivermead post-concussion symptoms questionnaireCognitionActivities of daily livingInterquartile rangeMedicineCognitive testMontreal Cognitive AssessmentCognitive skillPsychologyNeuropsychologyEffects of sleep deprivation on cognitive performancePhysical therapyAudiologyClinical psychologyPsychiatryCognitive impairmentInternal medicine

Abstract

fetched live from OpenAlex

Background No prevalence data on cognitive outcomes are available for general neurosurgical patients and few studies have assessed the correlation between common cognitive assessment tools of the occupational therapists and activity of daily living (ADL) at 1 year. Methods Consecutive neurosurgical patients with intrinsic brain lesions (brain tumours, traumatic intracerebral haematomas, spontaneous intracerebral haematomas, and cerebral arteriovenous malformations) were approached for consent to participate in the present study. Results At 1 year, the Montreal Cognitive Assessment score (mean ± standard deviation) was 20.4 (±8.6) and 42% of the patients had scores less than 22. The median number of the Neurobehavioral Cognitive Status Examination domains below the cutoff values was 8 (interquartile range: 3.5–9.75). Conclusions The cognitive assessments–-the Montreal Cognitive Assessment, the Frontal Assessment Battery, and the Rivermead Behavioural Memory Test–-showed satisfactory discriminating power for complete independence in instrumental ADL. Instrumental ADL was best correlated with the Rivermead Behavioural Memory Test.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.181
GPT teacher head0.399
Teacher spread0.218 · 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 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

Citations9
Published2011
Admission routes1
Has abstractyes

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