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Record W3113289283 · doi:10.3329/cardio.v13i1.50566

Economic Implications of Coronary Arterial Revascularization from Bangladesh Perspective

2020· article· en· W3113289283 on OpenAlexaff
Refaya Rashmin, Nazmul Hosain

Bibliographic record

VenueCardiovascular Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsConventional PCIMedicinePercutaneous coronary interventionStentRevascularizationCardiologyPovertyEmergency medicineArteryPaymentInternal medicineFinanceEconomic growthMyocardial infarctionBusiness

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.260
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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