Acute Coronary Syndromes and Inflammatory Bowel Disease: The Gut–Heart Connection
Bibliographic record
Abstract
(1) Background: Inflammatory bowel disease (IBD) induces a process of systemic inflammation, sharing common ground with acute coronary syndromes (ACS). Growing evidence points towards a possible association between IBD and an increased risk of ACS, yet the topic is still inconclusive. Therefore, we conducted a systematic review aiming to clarify these gaps in the evidence. (2) Methods: We conducted a systematic search on EMBASE, Cochrane Library, and PubMed, identifying observational studies published prior to November 2020. The diagnosis of IBD was confirmed via histopathology or codes. Full articles that fulfilled our criteria were included. Quality assessment was performed using the Newcastle-Ottawa scale (NOS). (3) Results: We included twenty observational studies with a total population of ~132 million subjects. Fifteen studies reported a significant association between ACS and IBD, while the remaining five studies reported no increase in ACS risk in IBD patients. (4) Conclusions: ACS risk in IBD patients is related to hospitalizations, acute active flares, periods of active disease, and complications, with a risk reduction during remission. Interestingly, a general increase in ACS risk was reported in younger IBD patients. The role of corticosteroids and oral contraceptive pills in increasing the ACS risk of IBD patients should be investigated.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".