Long term experience with a novel interventional cardiology network model: Learned lessons
Bibliographic record
Abstract
Objective: Many studies have assessed ischemic heart disease due to its high prevalence, secondary morbidities, high death rate, economic and social impact. We propose a novel model of intervention, the central objective of which is to guarantee the equal opportunity, avoiding patient transport and improving the use of resources assigned to cardiac care, ensuring patient safety and efficiency.Methods: We projected a model in which interventional cardiologists based at a high-volume center (Madrid, Hospital Clínico San Carlos [HCSC]) established an alliance with two other hospitals (Leganés, Hospital Severo Ochoa [HSO] and Alcalá, Hospital Príncipe de Asturias [Hospital Príncipe de Asturias ]), creating the opportunity to install a catheterization laboratory at each hospital (satellite units). We reviewed the clinical and economic long-term results, together with local hospital satisfaction levels.Results: Between 2000 and 2014, 63,817 cardiac procedures: 54,516 at HCSC, 7,618 at HSO (since 2003) and 1,683 at HUPA (since 2012) were performed. Using a random sample obtained between 2011-2012 assessing 737 percutaneous coronary interventions (PCI) classified according to the patient’s residence. No significant differences in bleedings during the first year (3.2% vs. 1.2%; p = .29), readmissions for a new myocardial infarction (5.7% vs. 3.5%; p = .41) or any-cause mortality (0.7% vs. 0%, p = .418)were observed. Subsequent scoring by professionals revealed both a high degree of satisfaction with the model and significant cost-savings implementing this network.Conclusions: A network on interventional cardiology is a sustainable experience in our environment, offering a high standard of patient-centered care quality, as required by health authorities and national and international scientific-societies. It reduced costs, and was perceived with an excellent degree of satisfaction by professionals and managers of the peripheral centers.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".