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Record W4312252692 · doi:10.4103/0019-5545.341716

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2022· article· en· W4312252692 on OpenAlexaboutno aff
Ragini Singhania, Sabita Dihingia, Dhrubajyoti Bhuyan, Kavery Bora

Bibliographic record

VenueIndian Journal of Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)Schizophrenia (object-oriented programming)Depression (economics)Depressive symptomsMedicineDrug-naïvePsychiatryIntervention (counseling)DrugAnxiety

Abstract

fetched live from OpenAlex

Background: Depressive symptoms though frequently seen in patients of schizophrenia are often missed. Presence of these symptoms is usually seen to worsen the quality of life in them. Aim: To assess presence of depressive symptoms and quality of life in drug naive cases of schizophrenia. Methods: The study was a hospital based cross sectional study conducted in the Department of Psychiatry, Assam Medical College and Hospital, Dibrugarh, Assam involving 57 consecutive drug naive schizophrenic patients for a period of one year between 18-60 years old. Calgary Depression Scale for Schizophrenia (CDSS) and Quality of Life Scale (QLS) were used as instruments. Results: 64.9% of patients in the study were found to have depressive symptoms. The overall mean score for quality of life was found to be 58.93 (SD= 20.51). In patients with depressive symptoms, CDSS mean score was 12.49 (SD=4.51) and quality of life mean score was 47.86 (SD=15.32). The QLS score was significantly correlated with CDSS score (r=-0.91, p<0.0001). Conclusion Early intervention in depressive symptoms in the schizophrenic patients will improve quality of life amongst such patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.147
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.011
Science and technology studies0.0020.000
Scholarly communication0.0090.004
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.8530.784

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 head0.012
GPT teacher head0.277
Teacher spread0.265 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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