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A cross sectional study on the association between new onset of psychiatric symptoms with severity of cognitive impairment in elderly patients

2019· article· en· W2952173850 on OpenAlexaboutno aff
Jery Antony, Anisha Nakulan, Shiny John

Bibliographic record

VenueInternational Journal of Research in Medical Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychiatryDementiaAnxietyCross-sectional studyPopulationCognitionCognitive impairmentClinical psychologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Background: Neuropsychiatric impairments play significant roles throughout the course of cognitive decline mainly in older adults with dementia or mild cognitive impairment (MCI). This study was aimed to find the association between psychiatric comorbidities and severity of cognitive impairment in elder patients presented with new onset of Psychiatric Symptoms.Methods: A cross sectional study was done among elder subjects (≥60 years of age) presented with new onset of psychiatric symptoms during one year period. Mini International neuropsychiatric interview and Montreal Cognitive Assessment scale were used for psychiatric diagnosis and severity of cognitive impairment grading, respectively. Association between psychiatric comorbidities and MCI was statistically analyzed.Results: Total 67 subjects were included in the study. Analysis of the psychiatric diagnosis revealed that major depressive episode (52.2%) was the most prevalent psychiatric disorder among the study population followed by Psychotic disorders (23.9%). Generalized anxiety disorder contributed to 19.4% of the total study population. Significant association (p<0.002) was identified between the severity of cognitive impairment and the psychiatric comorbidities.Conclusions: A significant association was identified between the severity of MCI and the psychiatric comorbidities. Major depressive episode was the most prevalent psychiatric disorder followed by psychotic disorders and generalized anxiety disorder.

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)0.011
metaresearch head score (Gemma)0.002
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 score0.013
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.449
Teacher spread0.402 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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