Academic productivity in pediatric neurosurgery in relation to elective surgery slowdown during the COVID-19 pandemic
Notice bibliographique
Résumé
OBJECTIVE: COVID-19 has not only impacted healthcare systems directly via hospitalizations and resource utilization, but also indirectly via adaptations in healthcare practice, such as the evolution of the academic environment and the rise of telemedicine and virtual education. This void in clinical responsibilities has been filled with academic productivity in various fields. In this study the authors investigate the influence of COVID-19 on the academic focus within pediatric neurosurgery. METHODS: All data were obtained from the Journal of Neurosurgery: Pediatrics (JNS Peds). The number of submissions for each month from January 2017 to December 2021 was collected. Data including number of publications, publication level of evidence (LOE), and COVID-19-related articles were collected and verified. Each publication was categorized by manuscript and LOE according to adaptations from the Canadian Task Force on Periodic Health Examination. Publication groups were categorized as pre-COVID-19 (January 2017-February 2020), peri-COVID-19 (March 2020-July 2020), and post-COVID-19 (August 2020-December 2021). Statistical analysis was performed to compare pre-COVID-19, peri-COVID-19, and post-COVID-19 academic volume and quality. RESULTS: During the study time period, a total of 3116 submissions and 997 publications were identified for JNS Peds. Only 2 articles specifically related to COVID-19 and its impact on pediatric neurosurgery were identified, both published in 2021. When analyzing submission volume, a statistically significant increase was seen during the shutdown relative to pre-COVID-19 and post-shutdown time periods, and a significant decrease was seen post-shutdown relative to pre-COVID-19. LOE changed significantly as well. When comparing pre-COVID-19 versus post-COVID-19 articles, a statistically significant increase was identified only in level 4 publications. When analyzing pre-COVID-19 versus post-COVID-19 (2020) and post-COVID-19 (2021), a statistically significant decrease in level 3 and increases in levels 4 and 5 were identified during post-COVID-19 (2020), with a rebound increase in level 3 and a decrease in level 5 during post-COVID-19 (2021). CONCLUSIONS: There was a significant increase in manuscript submission during the initial pandemic period. However, there was no change during subsequent spikes in COVID-19-related hospitalizations. Coincident with the initial surge in academic productivity, despite steady publication volume, was an inverse decline in quality as assessed by LOE.
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 | 0,028 | 0,165 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,013 | 0,019 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».