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Record W4296999561 · doi:10.3389/fpsyt.2022.976035

Timing of antipsychotics and benzodiazepine initiation during a first episode of psychosis impacts clinical outcomes: Electronic health record cohort study

2022· article· en· W4296999561 on OpenAlexaff
Maite Arribas, Marco Solmi, Trevor Thompson, Dominic Oliver, Paolo Fusar‐Poli

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersMedical Research CouncilKing's College London
KeywordsPsychiatryPsychosisBenzodiazepineCohortElectronic health recordMedicineSchizophrenia (object-oriented programming)Health recordsAntipsychoticMental healthCohort studyPsychologyInternal medicineHealth carePolitical science

Abstract

fetched live from OpenAlex

The impact of timing of antipsychotics and benzodiazepine treatment during a first episode of psychosis on clinical outcomes is unknown. We present a RECORD-compliant electronic health record cohort study including patients (n = 4,483, aged 14–35) with a primary diagnosis of any non-organic ICD-10 first episode of psychosis at SLAM-NHS between 2007 and 2017. The impact of antipsychotic timing (prescription > 1 week after a first episode of psychosis) was assessed on the primary outcome (risk of any psychiatric inpatient admission over 6 years), and secondary outcomes (cumulative duration of any psychiatric/medical/accident/emergency [A&E] admission over 6 years). The impact of prescribing benzodiazepine before antipsychotic at any point and of treatment patterns (antipsychotic alone, benzodiazepine alone, combination of antipsychotic with benzodiazepine) within the first week after a first episode of psychosis were also assessed. Survival analyses and zero-inflated negative binomial regressions, adjusted for core covariates, and complementary analyses were employed. Antipsychotic prescribed >1 week after a first episode of psychosis did not affect the risk of any psychiatric admission (HR = 1.04, 95% CI = 0.92–1.17, p = 0.557), but increased the duration of any psychiatric (22–28%), medical (78–35%) and A&E (30–34%) admission (months 12–72). Prescribing benzodiazepine before antipsychotic at any point did not affect the risk of any psychiatric admission (HR = 1.03, 95% CI = 0.94–1.13, p = 0.535), but reduced the duration of any psychiatric admission (17–24%, months 12–72), and increased the duration of medical (71–45%, months 12–72) and A&E (26–18%, months 12–36) admission. Prescribing antipsychotic combined with benzodiazepine within the first week after a first episode of psychosis showed better overall clinical outcomes than antipsychotic or benzodiazepine alone. Overall, delaying antipsychotic 1 week after a first episode of psychosis may worsen some clinical outcomes. Early benzodiazepine treatment can be considered with concomitant antipsychotic but not as standalone intervention.

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)0.005
metaresearch head score (Gemma)0.022
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.356
Teacher spread0.332 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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