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Record W2995525717 · doi:10.3126/njh.v3i3.26048

Factors Involved In First Myocardial Infarction, Its Complications And Thrombolytic Pattern In Selected Hospitals Of Nepal

2004· article· en· W2995525717 on OpenAlexaboutno aff
Ratan Shah, AB Upadhayaya, L.P. Tibrewala, Prakash Raj Regmi, Kiran Prasad Acharya, HH Khanal, S Rajbhandari, D Shrestha, Uttam Shrestha, Manoj Pandey

Bibliographic record

VenueNepalese Heart Journal · 2004
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiological transitionMyocardial infarctionMedicineDeveloping countryEpidemiologyCoronary heart diseaseQuarter (Canadian coin)Life styleDiseaseMedical emergencyEnvironmental healthCardiologyInternal medicineEconomic growthGeography

Abstract

fetched live from OpenAlex

World Health Organization [WHO] has predicted that by AD 2020 up to three- quarter of death in developing countries would result from non-communicable diseases (NCDs) and that Coronary Heart Disease (CHD) will top the list of killers. Data also indicate that epidemiological transition, which is characterized by aging and changing life style and culminates in epidemics of hypertension (HTN) and CHD, is rapidly occurring in India and other developing countries.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.143
GPT teacher head0.430
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2004
Admission routes1
Has abstractyes

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