Bibliographic record
Abstract
OBJECTIVE: Socioeconomic changes have imposed many administrative demands on neurosurgeons, including managing facilities such as the intensive care unit without absolute authority and maintaining referrals, marketing in an increasingly competitive environment, effecting change within stubborn hospital systems, negotiating fair contracts with insurance companies, using financial statements to make financial decisions, managing small groups under new rules of human resources, navigating a Byzantine system of reimbursement, and assessing entrepreneurial opportunities. A set of new tools and skills has been developed by the business community in response to similar problems that may be of use to neurosurgeons. These advances are reviewed in a neurosurgical context, and routes to business training for the neurosurgeon are discussed. METHODS: Recent advances in business are discussed with a focus on their relevance to neurosurgical practice. Current neurosurgical interest in business training and training opportunities for neurosurgeons are presented. RESULTS: Interest in business training within the neurosurgery community is keen, and advances in the field of business may be helpful in addressing the new tasks faced by neurosurgeons. CONCLUSION: New tools from advances in business are available which have been invaluable to corporations and may be helpful to neurosurgeons wanting to improve efficiency and maintain competitive advantage. Business training is available to neurosurgeons through a variety of routes.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".