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Record W2623647890 · doi:10.3329/bjpsy.v29i1.32743

Psychiatric morbidity among patients in cardiac outpatient department

2017· article· en· W2623647890 on OpenAlexaff
Bushra Sultana, Muhammad Zillur Rahman Khan, Sadya Tarannum, Nasim Jahan, Ahsan Ahmed, Md Faruq Alam

Bibliographic record

VenueBangladesh Journal of Psychiatry · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMedicineOutpatient clinicEmergency departmentCross-sectional studyPsychiatryEmergency medicineDiseasePediatricsInternal medicine

Abstract

fetched live from OpenAlex

Psychiatric morbidity among patients attending cardiac outpatient department has been revealed as a significant problem in many studies. The objective of this study was to determine the proportion of psychiatric morbidity among cardiac outdoor patients in National Institute of Cardio-Vascular Diseases (NICVD), Dhaka. It was a cross-sectional study conducted from September 2015 to February 2016 among the purposively selected patients of NICVD outdoor. Convenient sampling technique was used to select 151 patients aged 18 to 65 years who were attending the cardiac outpatient department in NICVD, Dhaka. A semi-structured questionnaire including Self-Reporting Questionnaire (SRQ) was used to screen psychiatric symptoms. Results showed that the mean (+SD) age of the patients was 46.09 (+11.17) years and majority of the respondents (60.9%) were male. The most common cardiac morbidity was ischemic heart disease (29.8%). Among all the respondents, 21.9% were suffering from psychiatric disorders. Maximum of the cases were diagnosed with major depressive disorder (11.3%). Thus, it was apparent that psychiatric morbidity was commonly present in patients who attended cardiac outpatient departments.Bang J Psychiatry June 2015; 29(1): 1-4

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.316
Teacher spread0.299 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

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