Invited Article: Is it time for neurohospitalists?
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.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".