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Record W2942055604 · doi:10.7202/1058611ar

Caractérisation des premiers épisodes de schizophrénie à partir de bases de données administratives de santé jumelées

2019· article· fr· W2942055604 on OpenAlexaffvenueabout
Mélissa Beaudoin, Stéphane Potvin, Laura Dellazizzo, Maëlle Surprenant, Alain Lesage, Alain Vanasse, André Ngamini-Ngui, Alexandre Dumais

Post-publication record

OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.

Bibliographic record

VenueSanté mentale au Québec · 2019
Typearticle
Languagefr
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeInstitut national de psychiatrie légale Philippe-PinelUniversité de SherbrookeInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalInstitut universitaire en santé mentale de Montréal
Fundersnot available
KeywordsHumanitiesMedicineGynecologyPhilosophy

Abstract

fetched live from OpenAlex

Schizophrenia (SCZ) is a severe chronic disease associated with significant functional impairments. Prior to a diagnosis of SCZ, some nonspecific symptoms may occur (i.e., anxiety, insomnia, depressive symptoms) and may progress into psychosis. While these may be attenuated or not of sufficient severity for psychosis, many will seek help for these symptoms. Understanding the predictors of SCZ remains a considerable challenge for clinicians. Thus, several studies have been conducted to explain the different premorbid trajectories of psychosis. Though, no consensus has been established on the prediagnostic characteristics of patients with SCZ and remains a matter of debate, especially for women and older patients. Hence, our study aims to clarify the psychiatric characteristics of patients from Quebec preceding their first episode of SCZ and address the influence of age and sex. To do so, we used administrative databases from the RAMQ (Physician billings) and MED-ÉCHO (hospital registry in Quebec) between January 1996 and December 2006; 98% of about 7.5 millions of inhabitants are registered with the universal health plan. It recorded 24,883 men and women over the age of 18 diagnosed with a first episode of SCZ between the years 2004 and 2007. Different psychiatric antecedents by groups of age and sex are reported by cumulative frequency. The sample comprised of 53% men. Approximately, 50% and 36% were diagnosed with SCZ by psychiatrists and family physicians respectively. Patients aged from 30 to 54 represented most of the sample; over half of men and women were first diagnosed after 30 years old. Those with no antecedents accounted for 65% of the sample, while overall 35% had at least one ICD-9 diagnosis, specifically and in descending order manic depressive psychosis, depressive disorder, and drug use disorder. In women under 30, anxiety, depressive disorder and adjustment disorder were more frequent. Whereas amongst men under 30, substance use disorder was the most common antecedent, followed by anxiety. Considering the total population coverage, these findings are interesting as they draw a representative global portrait of the population with SCZ before their diagnosis according to their age group and sex. This project recalls the importance of examining the first psychotic episodes to possibly intervene early in the course of the disease by addressing depressive disorders, anxiety disorders and substance use.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.870
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.315
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes3
Has abstractyes

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