Potential Explanatory Models of the Female Preponderance in Very Late Onset Schizophrenia
Bibliographic record
Abstract
Epidemiological and clinical studies have uniformly reported an overrepresentation of females with very-late-onset schizophrenia-like psychotic disorder (VLOS), in stark contrast to the sex distribution of early-onset schizophrenia. Various explanatory models have been proposed to account for these sex differences, including (a) antidopaminergic effects of estrogen, (b) differential vulnerability to subtypes, (c) neurodegenerative differences between the sexes, and (d) and sex differences in age-related psychosocial and neurological risk factors; however, these models have not yet been critically evaluated for their validity. Keywords related to VLOS symptomatology, epidemiology, and sex/gender were entered into the PubMed, MEDLINE, and Google Scholar databases spanning all years. Through a narrative review of symptomatology and pathophysiology of VLOS, we examine the strengths and limitations of the proposed models. We present a comprehensive biopsychosocial perspective to integrate the above models with a focus on the role of neuroinflammation. There is significant room for further research into the mechanisms of VLOS that may help to explain the female preponderance; the effects of estrogen and menopause, neuroinflammation, and dopaminergic transmission; and their interaction with age-related and lifetime psychosocial stressors and underlying biological vulnerabilities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".