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Record W2761653569 · doi:10.1177/0972063417727620

Screening of Chronic Alcoholics for Cognitive Impairment Using Montreal Cognitive Assessment—Occupational Therapy Perspective

2017· article· en· W2761653569 on OpenAlexaboutno aff
Parag Sawant, Preetee Gokhale, Zarine Ferzandi

Bibliographic record

VenueJournal of Health Management · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineCognitionCognitive impairmentOutpatient clinicPhysical therapyChronic alcoholicOccupational therapyActivities of daily livingPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Rationale: Chronic alcoholics suffer from cognitive impairment, thereby affecting their ADL and work performance. Objectives: The objective was to screen chronic alcoholics for cognitive impairment and assessing affectations in various components of MoCA. Early screening to help them from further deterioration in ADL and work. Materials and Methodology: In this case control study, 50 chronic alcoholics, males, age group 30–50 years, abstaining for more than two months, referred to outpatient De-addiction Occupational Therapy department of K. E. M. Hospital, Mumbai were screened using MoCA (Hindi) and compared with their age matched control group. Results: All patients showed mild cognitive impairment (MCI) on MoCA, irrespective of pattern and frequency of drinking. Their average mean score in MoCA test and control group was 21.02 and 26.03, respectively. The domains more affected were language, abstraction and memory, thus affecting their performance in ADL and work. Results were analyzed using unpaired ‘ t’-test and were statistically significant at the level of p < 0.05 and 95 per cent confidence level. Conclusion: Screening of chronic alcoholics using MoCA showed MCI with varying degree of affectations in the sub-components of the 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.129
GPT teacher head0.496
Teacher spread0.368 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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