Comparison of Serum Folate between Schizophrenic Patients and HealthyControls in Chinese Han Adult Population: A Systematic Review andMeta-Analysis
Bibliographic record
Abstract
AIM AND OBJECTIVE: To assess the relationship between serum folate and schizophrenia (SZ) risk in the Chinese Han adult population in different papers, a systematic review and metaanalysis were conducted. MATERIALS AND METHODS: We searched for this meta-analysis on three English databases (PubMed, Embase, and Web of science) and four Chinese databases (CNKI, SinoMed, Wanfang, and CQVIP) on March 27, 2021. INCLUSION CRITERIA: studies provided folate levels in serum of cases and controls as mean and standard deviation. EXCLUSION CRITERIA: subjects were not Chinese Han adult population. The Newcastle-Ottawa Scale score was used to assess the risk of bias in the included studies. Standard mean difference (SMD) was used to measure the difference between SZ patients and healthy controls. Subgroup analyses by measurement time, duration, and age were performed, respectively. RESULTS: This meta-analysis included 19 publications involving 1571 SZ cases and 1283 healthy controls. In total studies, the pooled result showed that SZ patients had decreased serum folate levels compared with healthy controls (SMD [95%CI] = -1.37[-1.83,-0.90], PSMD<0.001), and in most of the subgroups, the associations reached decreased significantly; while in the subgroup of drugs use, the association was not reached significantly. CONCLUSION: Dose-response analysis and subgroup analyses by gender were not performed due to the lack of data. Folate deficiency is associated with the patients, and antipsychotic drugs might have positive effects on improving serum folate levels in Chinese Han adult SZ.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".