Bibliometric evaluation of pediatric neurosurgery in North America
Bibliographic record
Abstract
OBJECT: The application of bibliometric techniques to academic neurosurgery has been the focus of several recent publications. The authors provide here a detailed analysis of all active pediatric neurosurgeons in North America and their respective departments. METHODS: Using Scopus and Google Scholar, a bibliometric profile for every known active pediatric neurosurgeon in North America was created using the following citation metrics: h-, contemporary h-, g-, and e-indices and the m-quotient. Various subgroups were compared. Departmental productivity from 2008 through 2013 was measured, and departments were ranked on the basis of cumulative h- and e-indices and the total number of publications and citations. Lorenz curves were created, and Gini coefficients were calculated for all departments with 4 or more members. RESULTS: Three hundred twelve pediatric neurosurgeons (260 male, 52 female) were included for analysis. For the entire group, the median h-index, m-quotient, contemporary h-, g-, and e-indices, and the corrected g- and e-indices were 10, 0.59, 7, 18, 17, 1.14, and 1.01, respectively; the range for each index varied widely. Academic pediatric neurosurgeons associated with fellowship programs (compared with unassociated neurosurgeons), academic practitioners (compared with private practitioners), and men (compared with women) had superior measurements. There was no significant difference between American and Canadian pediatric neurosurgeons. The mean Gini coefficient for publications was 0.45 (range 0.18-0.70) and for citations was 0.53 (range 0.25-0.80). CONCLUSIONS: This study represents the most exhaustive evaluation of academic productivity for pediatric neurosurgeons in North America to date. These results should serve as benchmarks for future studies.
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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 | 0.077 | 0.280 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.709 | 0.855 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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; both teacher heads agree on what is shown here.
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".