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Record W4404738111 · doi:10.1177/19418744241297187

Neurohospitalist Core Competencies

2024· review· en· W4404738111 on OpenAlexaff
Jana Wold, Jetter Robertson, Jerome Jeevarajan, Molly G. Knox, Prateek Thatikunta, Guillermo E. Solorzano, Kristin Galetta, Shefali Dujari, Tarini Goyal, Matthew Ehrlich, J. Donnelly, Elizabeth Marriott, Vishal Mandge, Roshni Dhoot, Matthew W. Luedke, Matthew B. Maas, Mei Yu, Michel B. Tolédano, Rafid Mustafa, Jamie L. Palaganas, Kathryn A. Kvam, Rachelle Dugue, Ethan Meltzer, Lahoud Touma, Maulik Shah, Vanja C. Douglas, Karen Orjuela, Brian J. Scott, Joshua P. Klein, David Likosky, Jennifer Simpson, Megan Richie, Carolin Dohle, Jane G. Morris, Carl A. Gold

Bibliographic record

VenueThe Neurohospitalist · 2024
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsCore competencyScope (computer science)MedicineScope of practiceSet (abstract data type)Psychological interventionMedical educationCurriculumHealth careInterpretation (philosophy)PsychologyNursingComputer sciencePedagogyManagement

Abstract

fetched live from OpenAlex

The Neurohospitalist Core Competencies comprise a set of competency-based learning objectives that encapsulate the knowledge, skills, and attitudes of neurohospitalitists who specialize in the care of hospitalized patients with neurologic conditions. These competencies serve to characterize the rapidly expanding field of neurohospitalist medicine. The 27 chapters are divided into 3 sections entitled: neurological conditions, clinical interventions and interpretation of ancillary studies, and neurohospitalist role in the healthcare system. Each individual learning objective in the chapters describes a specific concept with an action verb to illustrate the behavior that the neurohospitalist exhibits. The individual neurohospitalist may not exhibit mastery in each of the topics included as individual practices vary in scope and practice pattern. A few examples of how the complete set of competencies may be used include in the creation of curricula for neurohospitalist fellowships, to assist in defining the scope of practice of neurohospitalists for administrative leaders of hospitals and departments, and in influencing the direction of further research and quality improvement in the field.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.288
GPT teacher head0.474
Teacher spread0.186 · 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 designNot applicable
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

Citations3
Published2024
Admission routes1
Has abstractyes

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