Early Cognitive Impairment at Acute Stage After Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment after acute intracerebral hemorrhage (ICH) is common. While the evidence of early cognitive impairment at the acute stage after ICH is limited. We determined the frequency and risk factors of early cognitive impairment at the acute stage and investigated its association with delayed cognitive impairment after ICH. METHODS: A total of 208 patients with acute ICH were enrolled from January 2017 to February 2019. Cognitive function was assessed during the acute stage and at follow-up using Montreal Cognitive Assessment (MoCA) score. Significant cognitive impairment was defined as having a MoCA score <20 at the acute stage (within 1 week after hospital admission) or during follow-up. RESULTS: The mean observation period was 20 (IQC 17-23) months, and follow-up cognitive function data were collected from 185 patients. 89 (42.8%) and 86 (46.5%) patients had an acute stage and delayed significant cognitive impairment, respectively. Older age, large baseline hematoma volume, more severe ICH, and low level of education were significantly associated with significant cognitive impairment at the acute stage (all P ≤ 0.009). In the multivariable logistic regression model, the low MoCA score (odds ratio [OR] 0.59; 95% confidence interval [CI] 0.48-0.71; P<0.001) at the acute stage was independently associated with delayed significant cognitive impairment after ICH. CONCLUSION: Near half of the patients had significant cognitive impairment at the acute stage after ICH. Cognitive impairment is more frequent in the elderly, those with large baseline hematoma volume, and more severe initial neurological deficit. Having a lower MoCA score during the acute phase was independently associated with an increased risk of delayed cognitive impairment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".