Statins on Spontaneous Intracerebral Hemorrhage: A Meta-Analysis
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Objective. In order to explore whether the application of statins can improve the prognosis of patients with intracerebral hemorrhage. Methods. Studies of patients with intracerebral hemorrhage taking statins published in English until December 2021 were searched based on limited search terms, the retrieved literature was screened out based on inclusion and exclusion criteria, and the quality assessment and data extraction were carried out independently by two investigators. The extracted clinical data were then meta-analyzed. Results. A total of 17 literatures were included in this study, with a sample size of 16,988 cases, including 3,001 cases in the statin group and 13,487 cases in the control group. MRS score of mortality was used as the prognostic index to evaluate cerebral hemorrhage. According to the Newcastle-Ottawa Scale (NOS), the score of literature quality evaluation scale was 6–8, indicating good literature quality. Meta-analysis of clinical data extracted from the literature showed that the statin group reduced overall mortality after intracerebral hemorrhage compared with the nonstatin group ( P < 0.01 ). In terms of improving functional prognosis, the statin group improved functional prognosis 90 days after intracerebral hemorrhage ( P = 0.01 ). There was no significant difference between the statin and nonstatin groups in reducing the number of intracerebral hematomas. Conclusions. Statins can reduce the total mortality after ICH and improve the survival rate (90 d), without increasing the amount of hematoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".