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First episode psychoses in people over-35 years old: uncovering potential actionable targets for early intervention services

2024· article· en· W4399715873 on OpenAlexafffund
Maria Ferrara, Ilaria Domenicano, Aurora Marchi, Giulia Zaffarami, Alice Onofrio, Lorenzo Benini, Cristina Sorio, Elisabetta Gentili, Martino Belvederi Murri, Tommaso Toffanin, Julian Little, Luigi Grassi

Bibliographic record

VenuePsychiatry Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Ottawa
FundersUniversità degli Studi di FerraraEuropean CommissionUniversity of Ottawa
KeywordsMedicineIntervention (counseling)PsychosisPsychiatryEarly psychosisRetrospective cohort studyPediatricsInternal medicine

Abstract

fetched live from OpenAlex

The traditional youth-oriented design of Early Intervention Services (EIS) may lead to the exclusion of patients who have their psychotic onset later in life. A retrospective study was conducted to compare first-episode psychosis (FEP) patients who accessed treatment when aged ≤ 35 years with those ≥36+. A total of 854 patients were identified among 46,222 individuals who had access to community psychiatric services from 1991 to 2021. FEP were aged 18-65, received care between 2012 and 2021 and had a diagnosis of affective or non-affective FEP. Two groups were identified (FEP diagnosed at age ≤ 35 vs ≥ 36) and compared for sociodemographic and clinical characteristics. Most patients were diagnosed when aged ≥ 36+ (61.8%). Compared to the ≤ 35 group, older patients were more likely to be women, married and diagnosed with affective psychosis, and they were less frequently hospitalized. Long-acting injectables antipsychotics (LAI) were less frequently prescribed in the ≥ 36+ group, whereas antidepressants were more frequently prescribed compared to those aged ≤ 35. In both age groups, women were less frequently prescribed LAIs compared to men. These findings highlight the need to reorient EIS to accommodate the needs of older FEP, especially women.

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 score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.022
GPT teacher head0.373
Teacher spread0.350 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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