MétaCan
Menu
Back to cohort
Record W2751462851 · doi:10.22374/cjgim.v12i2.242

Point-of-Care Ultrasound in Internal Medicine

2017· article· en· W2751462851 on OpenAlexvenueno aff
Mitch Levine

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePoint of care ultrasoundClinical PracticeIntensive care medicineRadiologyFamily medicineUltrasound

Abstract

fetched live from OpenAlex

Over the past few decades a variety of technological advances have dramatically change the manner in which physicians practice medicine. Both clinically and administratively the practice of medicine is in evolution – for example; stents instead of surgery and digital health records instead of paper charts. For internal medicine physicians one of the biggest transformations with respect to how we will practice medicine is just on the horizon. The routine use of point-of-care ultrasound (PoCUS) will become an essential skill for the practicing internist. The application for PoCUS in the field of internal medicine is immense – accurately assessing the JVP in critically ill patients, performing arterial and venipunctures, diagnosing pericardial tamponade or determining the likelihood of a pulmonary embolus, or the diagnostic and therapeutic removal of body fluids, to name a few.

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)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.045
GPT teacher head0.367
Teacher spread0.322 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of General Internal MedicineSame topicUltrasound in Clinical ApplicationsFrench-language works237,207