Notice bibliographique
Résumé
Learning to be a neurosurgeon is an expansive process. We all know the myriad disciplines involved in the traditional elements of neuroanatomy, neurophysiology, surgical principles, disease states, and therapeutic approaches. Indeed, this expansive universe is a topic I have discussed before. It is truly getting larger. I have mentioned previously that some call time the “principle property of medicine.” We simply do not have the time to learn all of the care elements that affect our patients and are in constant flux. If we did, we would spend time in training at the side of neuroradiologists, learning their approaches to imaging-based diagnosis and study of outcomes, which is at the heart of everything we do. We would spend time at the side of physical medicine and rehabilitation specialists, physical therapists, and pain management experts, who we rely on to help bring our patients through difficult times and periods of restoration to better understand what is possible. We would spend months with neuropathologists to better know our disease states, as I thankfully did in my residency (I even had to do the autopsies). We would spend time with psychiatrists or cognitive neurologists, working to understand the workings of the functional mind (now increasingly prevalent in reports about tractography and surgical outcomes). I could go on and on. A 2-week elective in neuroradiology at the Toronto General Hospital as a fourth-year medical student, sparked my career in some ways, and put me on better ground to more comfortably take in house call by myself even at a young stage. Like most of us still do, we had to read our own scans and report to the chief resident. Later, during my own research in stroke repair and neuronal transplantation, I relied on physical medicine and rehabilitation colleagues to develop tests and programs necessary to “exercise the transplanted neurons.” As part of clinical trial design and conduct, I was opened up to that field in a way I had not been before. In decades past, neurosurgery training had broader exposure to those disciplines, which seemed to go away once we had to learn more and more new techniques. Again, time was the driver. We have certain confines within our journals and of course are dependent upon whatever articles are submitted to us for review and possible acceptance. As part of our editorial board, we do have sections in neurology, neuroscience, neuroradiology, neuropathology, showing interest and outreach to those disciplines. But clearly we can do more. Dan Kelly and colleagues wrote on the neuroscience of psychedelic agents that may have a role in clinical practice.1 Is this neurosurgery? Not yet. But it could be part of a patient or family discussion during recovery from a neurological insult. Are we prepared for that discussion? Our editorial board thought that exposure to the topic in our journal was warranted. At the time of this writing, together with Global Neurosurgery section editor, Gail Rosseau, I was allowed to view a screening of a new documentary on vaccine access called “Shot in the Dark.” This was during United Nations week here in New York as part of an initiative partly supported by the World Health Organization. The physicist and cosmologist Neil DeGrasse Tyson is an executive producer, and we had a chance to chat during the screening. We talked about parallels between neurosurgery and astrophysics and how surgery and our solutions are built increasingly on physics, mathematics, and geometry. Endovascular techniques and devices reflect that well. The film emphasized the cultural basis of vaccine acceptance or denial, and although this branch of medicine is not part of my own practice, it clearly emphasized the cultural elements that reflect on how patients accept our own recommendations, whether they be related to end-of-life care, brain tumor management, blood transfusion, or any perceived ethical conflict. Indeed on the topic of misinformation (the film shows the basis for how autism and vaccines became linked in the minds of some people and how misinformation led to tragic results), we all have our patients and families who read “literature” that seem to argue for or against treatments based on the interpreted writings. I am not speaking about equipoise, but about what some describe as misleading or even fraud. Associate Editor Fred Barker spoke on the topic of “misinformation” in his recent presidential address for the American Academy of Neurological Surgery. He argued that a powerful source of misinformation can be our own literature, misusing statistics which can cloud even articles published in this journal. Despite our efforts to ensure that statistical tests are appropriate, that P values are followed by CIs and that proper conclusions are drawn, we sometimes fall short. Some in the field of scholarly publication think that artificial intelligence techniques may solve some of these concerns, and they may be right. When use of statistics seems even too complex for our peer review editorial panel, we send articles to one of our team of biostatistician reviewers. All this toward the goal of making our message sound. As a member of the Accreditation Council for Graduate Medical Education Review Committee for Neurosurgery, I get to see the breadth of training program designs in the United States. Despite standards, they are not all the same, and some are being wonderfully innovative. As a former Director of the American Board of Neurological Surgery, I had the opportunity to participate in the evaluation of our trainees and program graduates at the individual level. Some of the best oral examination candidates displayed mastery of the classic disciplines in their responses, understanding the fundamentals of anatomy and physiology. Responses were more confident, more multidimensional, more diverse. There are many ways to become a neurosurgeon, with many paths and much to learn. It seems that increasingly there are multiple ways to care for the disorders we treat. It is clear that we can always do better if what we learn is focused on our patients, both medically and holistically. Neurosurgery is not always what we think of within our “traditional” definition. Our journals should remain open to this challenge. Douglas Kondziolka, MD, MSc Editor-in-Chief, Neurosurgery Publications New York, New York, USA
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 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,059 | 0,022 |
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 ».