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Record W4389097475 · doi:10.24908/pocus.v8i2.16316

Obstetric-Focused POCUS Training for Medical Students

2023· article· en· W4389097475 on OpenAlexvenueno aff
Koral Cohen, Jennifer Kidd, Emily Schiller, Agata Kantorowska, Wendy Kinzler, Martin Chavez

Bibliographic record

VenuePOCUS Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPoint of care ultrasoundCurriculumMedicineMedical educationUltrasoundMedical physicsPsychologyRadiologyPedagogy

Abstract

fetched live from OpenAlex

Point of care ultrasound (POCUS) is rapidly expanding throughout the United States. Due to its ability to quickly and accurately diagnose and guide therapy for critical conditions, POCUS is becoming routine in many specialties, with established guidelines in fields such as emergency medicine and critical care 1, 2, 3. For example, a study entitled "Ultrasound Integration in Undergraduate Medical Education: Comparison of Ultrasound Proficiency Between Trained and Untrained Medical Students" initiated an Emergency Medicine POCUS curriculum for first-year medical students that showed an increase in ultrasound capability 4. In short, as POCUS becomes more common practice, medical schools are beginning to implement POCUS training into their undergraduate medical education; studies from these institutions demonstrate that implementing a formal ultrasound curriculum into preclinical medical education significantly increases medical students' POCUS capabilities4, 5 and assisted in their understanding and learning of anatomy 6, 7.

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)0.003
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.009

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 head0.126
GPT teacher head0.441
Teacher spread0.316 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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