Academic impact and rankings of American and Canadian neurosurgical departments as assessed using the h index
Bibliographic record
Abstract
OBJECT: The authors undertook a study to estimate the relative academic impact of neurosurgical departments in Canada and the US using the h index, a measure of the number of citations received by a collection of work. METHODS: The study included 99 departments of neurosurgery with residency programs participating in the US National Residency Matching Program, and the 14 analogous Canadian programs. Three types of h indices were determined-one reflecting the cumulative work attributed to a neurosurgical department, h(c); one restricted to the cumulative work published over the past 10 years, h(10); and one limited to work published in 2 major North American neurosurgical journals, h(NS)(10). For an article to be included, attribution to a neurosurgical department had to appear in the address field in the database Thomson's ISI Web of Science. The three h indices were compared with each other, and their relation to other measures such as size of the department, degrees held by the faculty, and research funding was examined. RESULTS: Significant correlations were found between the citation indices and faculty size, number of publications and the types of degrees held by the faculty, and funding by the US NIH. Three types of authorship were identified: neurosurgeon, nonclinician researcher, and nonneurosurgeon clinical affiliate. The degree to which the latter 2 nonneurosurgeon categories contributed to the departmental h index varied among departments and can confound interdepartmental comparison. Limiting articles to those published in neurosurgical journals appeared to correct for the influence of nonneurosurgeons in departmental impact and reflect neurosurgeon-driven scholarship. CONCLUSIONS: The h index may be useful in evaluating output across neurosurgery departments.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.023 | 0.037 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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; 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".