Association between Graduate Degrees and Publication Productivity in Academic Neurosurgery
Bibliographic record
Abstract
OBJECT: Many neurosurgeons pursue graduate degrees as part of their training. In some jurisdictions, graduate degrees are considered a necessary condition of employment in academic neurosurgery. However, the relationship between possession of a graduate degree and eventual research productivity is not well established. We used bibliometric methods to analyze publications from academic Canadian neurosurgeons, with an emphasis on level of graduate training. METHODS: All neurosurgeons holding academic appointments at Canadian institutions from 2012-2016 were included. Over that time frame, Scopus was used to quantify the number of papers, number of citations, 5-year h-index and 5-year r-index, CiteScore, authorship position, and paper type (clinical or basic science). Publication output was compared between neurosurgeons grouped as MD-only, MD-Masters, or MD-PhD. RESULTS: In total, 2557 abstracts from 131 Canadian neurosurgeons were analyzed. We found that MD-Masters neurosurgeons published significantly more total papers, clinical papers, and first/last author papers than MD-only neurosurgeons. MD-PhD neurosurgeons had the same findings, in addition to more basic science papers, in journals with a higher CiteScore, 5-year h-index, and 5-year r-index than both other groups. These results were preserved even with significant outliers removed. There was no difference if graduate degrees were obtained before or after starting residency. There was no correlation with career length and number of recent papers published. CONCLUSION: The attainment of a graduate degree has an important association with future publication productivity for academic neurosurgeons. These data should be useful for hiring committees considering the value of graduate degrees from applicants for positions in academic neurosurgery.
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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 | 0.011 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.068 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".