Bibliographic record
Abstract
OBJECTIVE: To better understand premenstrual exacerbations of schizophrenia in women and weigh treatment options. METHOD: A PubMed literature search was conducted, using the search terms 'schizophrenia', 'psychosis', 'menstrual exacerbation', 'hormones' and assessing relevance to premenstrual exacerbation of schizophrenia symptoms. RESULTS: Exacerbations are usually distinguishable from periodic or menstrual psychosis, a relatively rare condition. Controversy continues about whether low estrogen periods of the month lead to an increase in schizophrenia symptoms among women of reproductive age or whether some women suffer from both schizophrenia and premenstrual dysphoric disorder (PMDD). No treatment trials of specific interventions have been conducted so that physicians must decide on a case-by-case basis whether to raise antipsychotic doses premenstrually, try estrogens or estrogen/progesterone combinations or selective estrogen receptor modulators, or target PMDD symptoms. CONCLUSION: Clinicians need to be aware of premenstrual symptom aggravation in a large minority of women with schizophrenia. Treatment strategies will depend on the nature of the symptoms that are exacerbated. Optimal treatment needs to be adjusted to the individual woman.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".