Baseline serum prolactin in drug-naive, first-episode schizophrenia and outcome at five years: is it a predictive factor?
Bibliographic record
Abstract
OBJECTIVE: Serum prolactin is influenced by antipsychotic use but its relationships with psychopathology and general functioning are not clear. This study aimed to assess these relationships. DESIGN: Serum prolactin levels were measured in patients with schizophrenia before being treated with antipsychotics and at various follow-up points. SETTING: The study was conducted in a nongovernmental psychiatric treatment center in Mumbai, India. PARTICIPANTS: The participants included 30 male and 30 female drug-naïve patients with schizophrenia and 31 control participants. MEASUREMENTS: The severity of psychopathology at baseline, three weeks, six weeks, and five years following treatment was assessed using a modified Brief Psychiatric Rating Scale. The Global Assessment of Functioning questionnaire was used at baseline and five years follow up. RESULTS: Contrary to our hypotheses, prolactin levels in male but not female patients at baseline were twice those of control volunteers. Correlations between prolactin, Brief Psychiatric Rating Scale, and Global Assessment of Functioning measurements were not significant for any time point up to six weeks, but were only significant at the five-year follow-up appointments, indicating that those patients with higher levels of serum prolactin had a better outcome at five years. CONCLUSION: Baseline serum prolactin levels in drug-naive patients with schizophrenia may be used for long-term prognosis, but are not reliable indicators of psychopathology and prognosis in the short term. Future research is needed to conclude with confidence whether or not prolactin can be used as a biomarker of psychopathological and overall functioning in schizophrenia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".