Notice bibliographique
Résumé
As the year comes to a close, it is time to reflect fondly on the revolutionary reports in the world of scientific publishing. To me, the most exciting were the findings from the Cassini spacecraft diving within Saturn's rings before destroying itself in its upper atmosphere. This so-called ‘Cassini Grand Finale’ had begun with the launch of the spacecraft over 20 years ago with the hope of finding subsurface water and potentially habitable environments on Saturn's moons 1. Our search for intelligent life continues, driven by advances in new technology. Back on earth, modern microscopy can allow single molecules to be observed and genomes can be precisely manipulated by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-mediated gene editing. The handling of the large data that are generated is likely to be enhanced by the ever-evolving role of artificial intelligence (AI) 2. This is the year when we have heard more about AI within the surgical community than any other 3. Most of us carry AI devices in our pockets in the form of our mobile phones. How can we use this to our benefit perhaps during the few minutes that we have between cases on a busy urological operating list? My usual trick is to ask ‘Siri’ (Speech Interpretation and Recognition Interface) on my iPhone® (Apple Inc., Cupertino, CA, USA) to play me a BJUI podcast, which provides me with a summary of a new paper without having to read any text. Many have told me that listening is becoming as fashionable as reading text, and this is one of our attempts at using AI to augment the BJUI experience. We also set ourselves the target of becoming one of the first journals to embrace and embed AI. With this in mind, I requested Andrew Hung from California to join the BJUI as Consulting Editor for AI. Andrew has already been publishing novel and often paradoxical reports on surgical performance based on automated performance metrics. A team from Canada has found that machine-learning (a subset of AI) algorithms can predict biochemical recurrence after radical prostatectomy more accurately than traditional statistical modelling 4. While being excited by these results, Hung 5 reminds us that this needs to be validated externally in a larger patient population before it is ready for prime time. Next year we hope to report more from the world of AI and perhaps even surprise our readers with embedded technology within the BJUI itself. With such rapid advances in science and technology comes the description of a new kind of education for our generation and the next. Joseph Aoun 6, who leads Northeastern University, describes this as ‘Humanics’ in his new book on higher education in the age of AI. It involves the fundamental difference between what machines and AI can do better than humans but equally what humans do better than machines. This book is a must-read, as it describes the pillars of technological, data and human literacy. So much so that I have started advising my scientifically minded students and colleagues to consider participating in short boot camps on data science. I wish you all, wherever you are and whatever the weather, much happiness and greetings of the season! None.
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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,016 |
| Communication savante | 0,012 | 0,009 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,005 |
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 ».