Taking the next step in publication productivity analysis in pediatric neurosurgery
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,096 | 0,321 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,047 | 0,061 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,015 | 0,010 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».