Characteristics and career outcomes of Neurosurgery Research and Education Foundation research fellowship recipients
Notice bibliographique
Résumé
OBJECTIVE: The American Association of Neurological Surgeons (AANS) Neurosurgery Research and Education Foundation (NREF) provides ongoing competitive research fellowships for residents and young investigators. The authors sought to determine the characteristics and career tracks of award recipients. METHODS: The authors analyzed characteristics and academic productivity parameters of NREF resident and young investigator awardees in the United States and Canada from 1983 to 2017. Data were extracted from the NREF database and online resources (Web of Science, NIH reporter). RESULTS: In total, 224 research grants were awarded to 31 women (14%) and 193 men (86%) from 1983 to 2017. Neuro-oncology (36%) was the most common research category. Sixty percent of awardees were in training and most resident award winners were in postgraduate year 5 (37%). Forty-nine percent of all awardees had an additional postgraduate degree (PhD 39%, Master's 10%) with a significantly higher number of PhD recipients being from Canada in comparison to any US region (p = 0.024). The Northeastern and Southeastern United States were the regions with the highest and lowest numbers of award recipients, respectively. More than one-third (40%) of awardees came from institutions that have a National Institute of Neurological Disorders and Stroke Research Education Grant (NINDS R25) for neurosurgical training. Awardees from NINDS R25-funded programs were significantly more likely to go on to receive funding from the National Institutes of Health (NIH) (40.4% vs 26.1%; p = 0.024). The majority of recipients (72%) who were no longer in training pursued fellowships, with a significant likelihood that fellowship subspecialty correlated with NREF research category (p < 0.001). Seventy-nine percent of winners entered academic neurosurgery practice, with 18% obtaining the position of chair. The median h-index among NREF winners was 11. NIH funding was obtained by 71 awardees (32%) with 36 (18%) being a principal investigator on an R01 grant from the NIH Research Project Grant Program. CONCLUSIONS: The majority of AANS/NREF research award recipients enter academics as fellowship-trained neurosurgeons, with approximately one-third obtaining NIH funding. Analysis of this unique cohort allows for identification of characteristics of academic success.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».