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Record W4410417636 · doi:10.1002/jhm.70042

The core competencies in hospital medicine: Procedures 2025 update

2025· article· en· W4410417636 on OpenAlexaff
Satyen Nichani, Megan Brooks, Christine Bryson, Nick Fitterman, Meltiady Issa, Michael Lukela, Nick Marzano, Kelly Sopko, Joseph R. Sweigart

Bibliographic record

VenueJournal of Hospital Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsGeorgetown Hospital
FundersSociety of Hospital Medicine
KeywordsMedicineHospital medicinePsychological interventionMEDLINEThoracentesisCompetence (human resources)Core competencyMedical educationNursingFamily medicineSurgeryPleural effusion

Abstract

fetched live from OpenAlex

This article presents an updated framework from the Society of Hospital Medicine for individual learning objectives related to key procedures in hospital medicine. Building upon the 2017 framework, these objectives have been revised to reflect evolving clinical evidence, advancements, and shifts in hospital medicine practice patterns. The methodology included a comprehensive literature review, expert consensus panels, and feedback from practicing hospitalists across diverse clinical settings. The updated learning objectives address procedural competencies for the most common interventions in hospital medicine, including arthrocentesis, emergency procedures, interpretation of chest radiographs and electrocardiograms, lumbar puncture, paracentesis, thoracentesis, and vascular access. These revised learning objectives provide a framework to guide curricular development, continuing medical education, and hospital medicine practitioners in developing and maintaining procedural competence essential for high-quality inpatient care.

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)0.023
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.067
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.003
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0020.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.004

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 head0.011
GPT teacher head0.304
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2025
Admission routes1
Has abstractyes

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