Predictors of an academic career among fellowship-trained spinal neurosurgeons
Bibliographic record
Abstract
OBJECTIVE: Although fellowship training is becoming increasingly common in neurosurgery, it is unclear which factors predict an academic career trajectory among spinal neurosurgeons. In this study, the authors sought to identify predictors associated with academic career placement among fellowship-trained neurological spinal surgeons. METHODS: Demographic data and bibliometric information on neurosurgeons who completed a residency program accredited by the Accreditation Council for Graduate Medical Education between 1983 and 2019 were gathered, and those who completed a spine fellowship were identified. Employment was denoted as academic if the hospital where a neurosurgeon worked was affiliated with a neurosurgical residency program; all other positions were denoted as nonacademic. A logistic regression model was used for multivariate statistical analysis. RESULTS: A total of 376 fellowship-trained spinal neurosurgeons were identified, of whom 140 (37.2%) held academic positions. The top 5 programs that graduated the most fellows in the cohort were Cleveland Clinic, The Johns Hopkins Hospital, University of Miami, Barrow Neurological Institute, and Northwestern University. On multivariate analysis, increased protected research time during residency (OR 1.03, p = 0.044), a higher h-index during residency (OR 1.12, p < 0.001), completing more than one clinical fellowship (OR 2.16, p = 0.024), and attending any of the top 5 programs that graduated the most fellows (OR 2.01, p = 0.0069) were independently associated with an academic career trajectory. CONCLUSIONS: Increased protected research time during residency, a higher h-index during residency, completing more than one clinical fellowship, and attending one of the 5 programs graduating the most fellowship-trained neurosurgical spinal surgeons independently predicted an academic career. These results may be useful in identifying and advising trainees interested in academic spine neurosurgery.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".