Serum multi-trace elements and post-stroke cognitive impairment: a prospective observational cohort study
Bibliographic record
Abstract
Post-stroke cognitive impairment (PSCI) significantly affects stroke survivors. Identifying modifiable risk factors for PSCI is essential. Serum multi-trace elements are crucial for neurological function but vary in concentration among older adults. It remains unclear whether increasing multi-trace elements can reduce the incidence of PSCI. We investigated the associations between baseline serum multi-trace elements and PSCI. The Montreal Cognitive Assessment defined PSCI. We used logistic regression analyses to evaluate the association between serum multi-trace elements and PSCI. Subsequently, we assessed the associations between serum multi-trace elements and three different cognitive domains using the Kruskal-Wallis test. We further evaluated improvements in the predictive ability of serum multi-trace elements. Finally, 626 patients (mean age: 62.85 ± 7.54 years) were followed up for a median of 1.2 years. Lower concentrations of serum iron (odds ratio [OR] = 2.498, 95% confidence interval [CI]: 1.505-4.145) and zinc (OR = 2.015, 95% CI: 1.233-3.293) were associated with a higher PSCI risk. Higher concentrations of serum iron (OR = 0.368, 95% CI: 0.227-0.595) and magnesium (OR = 0.273, 95% CI: 0.164-0.454), along with lower concentrations of serum copper (OR = 0.544, 95% CI: 0.34-0.872), were significantly correlated with a lower PSCI risk. Cognitive impairments varied across multi-trace elements. Serum iron affected wider cognition, while magnesium and copper levels were strongly associated with language and executive function. Adding serum multi-trace elements to the conventional model improved PSCI risk reclassification (area under curve: 0.676-0.718). Multi-trace elements may influence PSCI progression. This study was registered with the Chinese Clinical Trial Registry (URL: https://www.chictr.org.cn/ ; unique identifier: ChiCTR1900022675).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".