Patient reperfusion preferences in acute myocardial infarction: mortality versus stroke, benefits versus costs, high technology versus drugs.
Bibliographic record
Abstract
BACKGROUND: Reperfusion therapy, thrombolysis and primary percutaneous coronary intervention (PCI) decrease mortality in ST elevation acute myocardial infarction. Tissue plasminogen activator (tPA) reduces the risk of death but at an increased risk of stroke and cost compared with streptokinase (SK). PCI reduces the risk of death and stroke compared with tPA, but at increased costs. The authors explored patient preferences for the various reperfusion strategies. PATIENTS AND METHODS: Among patients hospitalized with an acute coronary syndrome, preferences for tPA or SK were determined using a questionnaire based on Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries (GUSTO-1) trial data including risk of death, stroke and the combination of the two. The impact of cost was assessed under the assumption of government or patient payment. Overall, the societal preference was solicited based on all the data. A similar survey was conducted comparing primary PCI with tPA using outcome data from a Cochrane review. RESULTS: When viewed in the context of net clinical benefit (NCB), 66.7% of patients chose tPA over SK. The preference for tPA diminished under the scenario of patient payment compared with government payment. However, as a societal strategy, the preference for tPA was 40.5% (P<0.001 versus NCB). Preference for primary PCI over tPA was strong whether based on risk of death (78.5%), stroke (88.1%) or NCB (95.4%). Cost considerations resulted in a slight fall in PCI preference (87.7%). As an overall societal strategy, 81.0% chose primary PCI over tPA (P=0.016 versus NCB). The preference for PCI was twice that for the most effective, but perhaps riskier, thrombolytic agent (tPA) (P<0.0001). CONCLUSIONS: Preference for the potentially inferior thrombolytic agent appears to depend on the lesser risk of stroke and the lower cost. Primary PCI was preferred by patients likely due to the lower risk of death and stroke, despite the increased cost. The preferences appeared to be influenced by societal costs. In addition, the allure and heightened expectations of high technology may play a role.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".