Disentangling early and late onset of psychosis in women
Bibliographic record
Abstract
Introduction Women present a second peak of incidence of psychosis during menopausal transition, partially explained by the loss of estrogen protection conferred during the reproductive years. Despite this, few studies compare sociodemographic, biological, clinical varibles and neurocognitive performance between women with early onset of psychosis (EOP) and those with late onset of psychosis (LOP). Objectives Our aim was to characterize both groups in a large sample of women, of which 294 were FEP patients (EOP = 205; LOP = 85) and 202 were healthy controls (HC) grouped following cutoff point (<>40 years of age) in previous studies. Methods Clinical and laboratory assessments were completed. Neurocognitive performance was also evaluated, and a cognitive global deficit score (GDS) was derived. ANCOVA was used for comparisons. Results EOP women were more frequently single and unemployed than comparable HC. Cholesterol levels in LOP women were higher than those of EOP women. LOP presented less severe symptoms, and higher scores in processing speed and premorbid IQ than EOP patients. Cannabis and alcohol use were also more frequent in EOP than LOP women. Conclusions Women with EOP and LOP show several sociodemographic, neuropsychological and clinical differences which may be valuable for planning personalized treatment emphasizing in socialization and differential generational dynamics. Some of these differences may be due to the aging process, while others might be influenced by factors such as lack of estrogen neuroprotection. In turn, drug consumption, low IQ and recent experienced trauma could as well reduce efficacy of hormonal neuroprotection. Disclosure No significant relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".