Bibliographic record
Abstract
Background: Physical and emotional stress have been associated with an increased incidence of acute coronary syndrome (ACS). Sporting events such as soccer matches can cause spectators to experience cardiovascular events. The objective of the present study was to determine whether an association of this type existed during a Spanish league competition. Methods: We recorded data from patients who were admitted with ACS during 2018 - 2020. Patients were divided into two groups: those who were admitted on the day the local team played and those who were admitted on nonmatch days. We determined various cardiovascular risk factors, including the degree of hostility and anxiety. Results: Away wins reduced the number of admissions with ACS by 30%, whereas a local loss increased hospitalizations by more than 30%. The profile of patient admitted on match days was a > 65 years old man, smoker (current or past), obese, with worse control of his hypertension, diabetes, and dyslipidemia, poor pharmacological adherence and high anxiety and hostility scores. Conclusions: A loss by the local team increases the number of admissions with ACS in males with a high burden of cardiovascular risk factors. Primary prevention measures should be taken to reduce the frequency of these events.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".