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Record W4396689129 · doi:10.24875/acm.23000215

Características de la formación de los cardiólogos en América Latina: una encuesta de la Sociedad Interamericana de Cardiología

2024· article· es· W4396689129 on OpenAlexaff
Ezequiel Lerech, Jean P. Carrión-Arcela, Cristhian E. Scatularo, Franklin E. Cueva-Torres, Melisa Antoniolli, Rodrigo Núñez-Méndez, Sebastián García-Zamora, Álvaro Sosa Liprandi, Adrián Baranchuk, Ezequiel Zaidel

Bibliographic record

VenueArchivos de cardiología de México · 2024
Typearticle
Languagees
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsQueen's University
Fundersnot available
KeywordsHumanitiesMedicineArt

Abstract

fetched live from OpenAlex

Objectives: Describe the characteristics of the different cardiology medical residencies in Latin America. Method: Cross-sectional study that aims to evaluate the characteristics of cardiology residencies in Spanish-speaking countries of Latin America, through self-administered electronic surveys. Results: Three hundred seven residents of 147 residences were surveyed. Mean age was 31 years and 63% were male. Ninety eight percent carry out their training in the capital city. The average total training time is 4.8 years. Forty four percent complete their residency in internal medicine prior to starting cardiology, and 10% have no prior training. In cardiology training is 3 years in most countries. Fifty four percent present academic activities every day and 16% only once or less, consisting of theoretical classes (93%), clinical cases (85%), bibliographic workshops (69%), and writing scientific papers (68%). Supervision is carried out by the chief resident (45%), resident coordinator (44%), resident instructor (27%) or the department head (54%), while 2.6% do not present supervision. The main rotations were echocardiography (99%), hemodynamics (96%), coronary unit (93%), and electrophysiology (92%). Residents highlighted the need to improve academic activities (23%) and scientific production (12%). Conclusions: There are important differences in the academic and practical training between the residences of the different countries of America.

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)0.016
metaresearch head score (Gemma)0.073
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.000

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 head0.426
Teacher spread0.398 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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