Abstract 247: Is Oxygen Therapy Beneficial in Acute Myocardial Infarction? A Meta-Analysis
Bibliographic record
Abstract
Background: Supplemental oxygen (O2) is beneficial in patients with acute myocardial infarction (AMI) who have hypoxemia. However in patients who have suspected AMI without hypoxemia, the benefit of O2 remains uncertain. We conducted a meta-analysis, including the recent landmark trials, to establish the efficacy and safety of O2 in AMI. Methods: We searched Medline, Embase, and Ovid SP databases for randomized clinical trials that compared O2 with room air in suspected AMI without hypoxemia. Hypoxemia was defined as O2 saturation of <90%. The primary endpoint was all-cause mortality. Data were pooled by the random effects model and I2 was used to assess for the heterogeneity. Results: A total of 6 studies including 8193 participants were included in the final analysis. On pooled analysis, there was no significant difference between the two arms in 30-day all-cause mortality (OR=1.10 [0.67, 1.80]; p=0.70), cardiogenic shock (OR=0.75 [0.49, 1.16]; p=0.19), recurrent myocardial infarction (OR=1.24 [0.46, 3.34]; p=0.67) and left ventricular ejection fraction (SMD= -1.95 [-4.76, 0.85]; p=0.17). Conclusion: Contrary to the routine clinical practice, our analysis shows that O2 administration does not provide any benefit in AMI patients who have normal oxygen saturation. It is reasonable to reserve O2 only for patients with AMI and hypoxemia.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".