Meta-analysis of the relationship between interleukin-6 levels and the prognosis and severity of acute coronary syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aimed to explore the relationship between plasma interleukin 6 (IL-6) levels, adverse cardiovascular events, and the severity of acute coronary syndrome (ACS). A literature review was performed of studies regarding IL-6 and ACS extracted from databases including EMBASE, Cqvip, MEDLINE, Web of Knowledge, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang data. The Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the literature. The literature was screened, its quality was evaluated, and relevant data were extracted for performing meta-analysis using RevMan software (version 5.3). A total of 524 studies were included in the initial survey. After several rounds of screening and analysis, six studies met the inclusion criteria and underwent meta-analysis using a fixed-effect model. Patients were divided into non-severe and severe groups based on the concentration of high-sensitivity C-reactive protein. Meta-analysis of the relationship between IL-6 and the severity of ACS showed that the plasma IL-6 level of patients in the severe group was significantly higher than that of patients in the non-severe group (p<0.00001). Additionally, patients with experience of major adverse cardiovascular events had significantly higher plasma IL-6 levels than did patients without experience of such events (p<0.00001). In summary, patients with ACS and high IL-6 levels tended to be in a critical condition, with a higher risk of adverse cardiovascular events and worse prognosis. Thus, IL-6 levels could indicate whether patients with ACS may have adverse cardiovascular events and determine the severity of ACS.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it