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Record W2167821196 · doi:10.1176/appi.ajp.159.1.133

Elevation of Prolactin Levels by Atypical Antipsychotics

2002· article· en· W2167821196 on OpenAlexaff
Peter Turrone, Shitij Kapur, Mary V. Seeman, Alastair J. Flint

Bibliographic record

VenueAmerican Journal of Psychiatry · 2002
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProlactinOlanzapineRisperidoneClozapineInternal medicineEndocrinologyAtypical antipsychoticMedicineDopamineAntipsychoticSchizophrenia (object-oriented programming)PsychologyPsychiatryHormone

Abstract

fetched live from OpenAlex

OBJECTIVE: Atypical antipsychotics are thought not to elevate prolactin levels. The authors examined data suggesting that atypical antipsychotics do elevate prolactin levels but more transiently than typical antipsychotics. METHOD: Prolactin levels in 18 male patients with schizophrenia who were receiving atypical antipsychotics were monitored over the 24-hour period following administration of their daily oral dose of risperidone, olanzapine, or clozapine. RESULTS: The baseline prolactin levels in patients receiving risperidone (mean=27 ng/ml, SD=14) were abnormally high, but baseline prolactin levels in patients receiving olanzapine (mean=9 ng/ml, SD=5) and clozapine (mean=9 ng/ml, SD=5) were not high. All three atypical antipsychotics caused a doubling of prolactin levels over baseline levels 6 hours after medication administration. CONCLUSIONS: These data suggest that these atypical antipsychotics raise prolactin levels, although the increases with olanzapine did not reach statistical significance. This suggests that the differences in the effects on prolactin levels of atypical and typical antipsychotics are not categorical but lie in the degree and duration of dose-induced prolactin elevation, attributable to the differential binding properties of each drug on pituitary dopamine D(2) receptors.

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)0.000
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 score0.275
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.300
Teacher spread0.279 · 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

Citations191
Published2002
Admission routes1
Has abstractyes

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