Invited Article: Is it time for neurohospitalists?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Explosive growth of hospital-based medicine specialists, termed hospitalists, has occurred in the past decade. This was fueled by pressures within the American health care system for timely, cost-effective, and high-quality care and by the growing chasm between inpatient and outpatient care. In this article, we sought to answer five questions: 1) What is a neurohospitalist? 2) How many neurohospitalists practice in the United States? 3) What are potential advantages of neurohospitalists? 4) What are the challenges of implementing a neurohospitalist practice? 5) What effect does a neurohospitalist have on clinical outcomes? METHODS: We queried biomedical databases (e.g., PubMed) by using the search terms "hospitalist," "neurohospitalist," and "neurology hospitalist." We also searched the Society of Hospital Medicine and the American Academy of Neurology Dendrite classified advertisement Web sites for hospitalist and neurology hospitalist growth by using the same search terms. RESULTS: We defined neurology hospitalists (neurohospitalists) as neurologists who devote at least one-quarter of their time managing inpatients with neurologic disease. Although the number of hospitalists has grown considerably over the past decade, limited data on neurohospitalists exist. Advertisements for neurohospitalist positions have increased from 2003 through 2007, but accurate assessment of growth is limited by the lack of a central organizational affiliation and unifying terminology. CONCLUSION: Health care pressures spawned the growth of medicine and pediatric hospitalists, who provide efficient, cost-effective care by reducing the length of hospitalization. Because neurologists experience the same pressures, we expect neurohospitalists to increase in number, especially within areas that have sufficient inpatient volume and resources.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it