Economic Implications of Coronary Arterial Revascularization from Bangladesh Perspective
Bibliographic record
Abstract
Background: There has been a radical improvement lately both for coronary artery bypass graft (CABG) and percutaneous coronary intervention (PCI) in Bangladesh. Besides the clinical goals, the economic impact of these procedures is very important as well. Out of pocket spending is the major payment strategy for healthcare in Bangladesh. It is estimated that the poverty headcount increased by 3.5% due to out of pocket healthcare payments. Methods: Data on patients’ expenditure for CABG and PCI in seven Bangladeshi hospitals were collected between 16th and 30th August, 2020. Several models were created, where the cost of CABG was compared with that of PCI in each of these hospital settings. Results: In the two public hospitals CABG is much cheaper than PCI. However, in mid-level expensive hospitals the cost of PCI with 2 stents is comparable with that of CABG, but with 3 or more stents, PCI becomes more expensive. In the big corporate hospitals, CABG tends to be relatively more expensive. The basic treatment expenditure of a patient suffering from triple vessel ischemic heart diseases may range from Taka 50000 to Taka 415000. Conclusion: In Bangladesh CABG is much cheaper than multi-stent PCI in the public and medium range private hospitals. CABG in corporate hospitals may be equal or even more expensive than PCI. IHD may contribute to national poverty as it may turn into a catastrophic health event for the patient’s family. Cardiovasc. j. 2020; 13(1): 56-61
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".