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Record W3021006541 · doi:10.21037/jtd.2020.03.104

Standardizing education in interventional pulmonology in the midst of technological change

2020· review· en· W3021006541 on OpenAlexaboutno aff
Waqas Aslam, Hans J. Lee, Carla Lamb

Bibliographic record

VenueJournal of Thoracic Disease · 2020
Typereview
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAccreditationStandardizationSubspecialtyMedical physicsCurriculumPulmonologyMedical educationPulmonologistEndobronchial ultrasoundGraduate medical educationRadiologyBronchoscopyIntensive care medicinePathologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

Interventional pulmonology (IP) is a maturing subspecialty of pulmonary medicine. The robust innovation in technology demands standardization in IP training with both disease and technology driven training. Simulation based training should be considered a part of IP training as seen in other procedural and surgical subspecialties. Procedure volume is a component of training; however, this does not guarantee or translate into competency for learners. Basic competency skills can be assessed using standardized well validated assessment tools designed for various IP procedures including flexible bronchoscopy, endobronchial ultrasound guided transbronchial needle aspiration (EBUS TBNA), rigid bronchoscopy and chest tube placement; however, further work is needed to validate tools in all procedures as new technologies are introduced beyond fellowship training. Currently there are at least 39 IP fellowship programs in the United States (US) and Canada which has led to improved training by accreditation of programs who meet rigorous requirements of standardized curriculum and procedural volume. The challenge is to be innovative in how we teach globally with intention and how to best integrate new evolving technology training for those not only during fellowship training but also beyond fellowship training.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.095
GPT teacher head0.473
Teacher spread0.378 · 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 designOther design
Domainnot available
GenreReview

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

Citations19
Published2020
Admission routes1
Has abstractyes

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