Neurohospitalist Core Competencies
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".