Taking the next step in publication productivity analysis in pediatric neurosurgery
Bibliographic record
Abstract
OBJECTIVE There has been an increasing interest in the quantitative analysis of publishing within the field of neurosurgery at the individual, group, and institutional levels. The authors present an updated analysis of accredited pediatric neurosurgery training programs. METHODS All 28 Accreditation Council for Pediatric Neurosurgery Fellowship programs were contacted for the names of pediatric neurosurgeons who were present each year from 2011 through 2015. Faculty names were queried in Scopus for publications and citations during this time period. The 5-year institutional Hirsch index [i h(5)-index] and revised 5-year institutional h-index [i r(5)-index] were calculated to rank programs. Each publication was reviewed to determine authorship value, tier of research, clinical versus basic science research, subject matter, and whether it was pediatrics-specific. A unique 3-tier article classification system was introduced to stratify clinical articles by quality and complexity, with tier 3 being the lowest tier of publication (e.g., case reports) and tier 1 being the highest (e.g., randomized controlled trials). RESULTS Among 2060 unique publications, 1378 (67%) were pediatrics-specific. The pediatrics-specific articles had a mean of 15.2 citations per publication (median 6), whereas the non-pediatrics-specific articles had a mean of 23.0 citations per publication (median 8; p < 0.0001). For the 46% of papers that had a pediatric neurosurgeon as first or last author, the mean number of citations per publication was 12.1 (median 5.0) compared with 22.5 (median 8.0) for those in which a pediatric neurosurgeon was a middle author (p < 0.0001). Seventy-nine percent of articles were clinical research and 21% were basic science or translational research; however, basic science and translational articles had a mean of 36.9 citations per publication (median 15) compared with 12.6 for clinical publications (median 5.0; p < 0.0001). Among clinical articles, tier 1 papers had a mean of 15.0 citations per publication (median 8.0), tier 2 papers had a mean of 18.7 (median 8.0), and tier 3 papers had a mean of 7.8 (median 3.0). Neuro-oncology papers received the highest number of citations per publication (mean 25.7). The most common journal was the Journal of Neurosurgery: Pediatrics (20%). MD/PhD faculty members had significantly more citations per publication than MD faculty members (mean 26.7 vs 14.0; p < 0.0001) and also a higher number of publications per author (mean 38.6 vs 20.8). The median i h(5)- and i r(5)-indices per program were 14 (range 5-48) and 10 (range 5.6-37.2), respectively. The mean i r(5)/i h(5)-index ratio was 0.8. The top 5 fellowship programs (in descending order) as ranked by the i h(5)-index corrected for number of faculty members were The Hospital for Sick Children, Toronto; Children's Hospital of Pittsburgh; University of California, San Francisco Benioff Children's Hospital; Seattle Children's Hospital; and St. Louis Children's Hospital. CONCLUSIONS About two-thirds of publications authored by pediatric neurosurgeons are pediatrics-specific, although non-pediatrics-specific articles averaged more citations. Most of the articles authored by pediatric neurosurgeons are clinical, with basic and translational articles averaging more citations. Neurosurgeons with PhD degrees averaged more total publications and more citations per publication. In all, this is the most advanced and informative analysis of publication productivity in pediatric neurosurgery to date.
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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 | 0.096 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.047 | 0.061 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".