In Reply: Clipping of Intracranial Aneurysms by Neurosurgical Trainees Is Safe and Effective: Statewide Retrospective Review of 614 Consecutive Cases in Queensland, Australia
Notice bibliographique
Résumé
To the Editor: We thank Rebchuk and colleagues1 for their correspondence and contribution of their experience in neurovascular training from the perspective of their Canadian institution. The letter highlights the challenge in generalization of our data to other systems including marked differences in several critical contextual factors. We note the differences between these two series in case load (eg, their most commonly clipped aneurysm arose from the anterior communicating artery), case selectivity (86% ‘trainee-led') and differing definitions of primary operator status. They also operate within a system that centralizes specialist neurovascular cases, rather than have that case-load spread among multiple centers. As neurosurgeons, our primary responsibility is to ensure the safety of each patient who is under our care, therefore training the next generation is a secondary responsibility. We are encouraged by proposals for innovative approaches to in vivo training such as the reported ‘4-hand technique’; however, the reporting of safety data to support novel approaches is a prerequisite to their wider uptake. In addition, the tension between service provision and training is a relevant consideration when calling for the centralization of services such as open aneurysm treatment. Although we agree that trainees are likely to benefit from centralization/consolidation of services that are in close geographical proximity, the provision of open cerebrovascular capability across the broad landmasses of Australia and Canada requires that regional/isolated neurosurgical centers retain that skillset. For example, one regional neurosurgical service in Queensland covers a geographical catchment area larger than the State of Texas. In our series, the elective and emergency aneurysm cases treated in regional centers were treated exclusively by senior (attending) neurosurgeons as the balance of priorities in that context favors service provision and the maintenance of the skillset by those senior surgeons.2 This tension is also seen in the setting of privatized health care where patient expectations and competition for patient volume make higher levels of intraoperative participation by trainees challenging to facilitate or, indeed, commercially problematic to promote. In these specific contexts, where trainee exposure is compromised by such practicalities, nontraditional training models may have their greatest impact. We are pleased to have reignited the conversation surrounding in vivo neurovascular training in the neurosurgical literature. We hope the publication of our data, and the experience of Rebchuk and colleagues will encourage other authors to contribute safety and outcome data to help support and refine in vivo training practices in cerebrovascular neurosurgery.
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 | 0,003 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».