Notice bibliographique
Résumé
Prior to the last century, surgeons perfected their craft through preceptorships. Early in the last century, Halsted introduced the German residency system of graded responsibility to North America.1 This system remains the cornerstone of surgical education. However, there has been tremendous change in surgical techniques, especially related to the practice of urology. We have witnessed the introduction of flexible ureteroscopy with laser lithotripsy and percutaneous nephrolithomy for the treatment of renal calculi. In addition, with the advent of laparoscopy and robotics, the surgical treatment of many urological diseases has changed dramatically. Despite this rapid evolution in urological practice, changes in surgical education and evaluation have lagged behind. The authors highlight the disparity in the use of novel teaching adjuncts among various programs.2 In addition, the incorporation of such adjuncts into formal assessments seems to be applied in a heterogeneous manner. Although many believe surgical expertise is a reflection of an individual’s intrinsic ability, empiric research has con-firmed the importance of practice.3,4 Simulators are instruments that reproduce, under artificial conditions, components of surgical tasks.5 Types of models include cadaver, animal, bench and computer software-based simulators. Cadaver models provide true anatomic representation, but the tissue quality might not be as realistic as living tissue. Live animals provide a model with appropriate tissue texture; however, anatomy may not be entirely representative of its human counterparts. Additionally, the use of human cadavers and live animals tends to be expensive and raises ethical issues. Bench models sacrifice fidelity for safety, availability, portability and lower overall cost.1,6 With advances in material technology and computer hardware and software, simulators have become more advanced, with higher fidelity and more capacity for assessment and feedback. Fidelity of a model refers to its realistic features. A simulator does not need to look realistic as long as the pertinent steps of the procedure are performed. That is, a low-fidelity simulator can provide the same benefit as a high-fidelity model.7 The most important outcome of simulators is the ability for skills learned on a model to be translated into improved performance in the clinical setting. This concept is referred to as transferability.3 Many bench and virtual reality simulators have been developed to reproduce many urological procedures.8 Some of these simulators are currently commercially available. While simulators can act as adjuncts for the acquisition of technical skills, they are no substitution to practice in real-life surgical conditions. The second aspect highlighted in this study is the lack of uniformity in assessing surgical skills across programs.2 The ideal components of a sound assessment test are reliability and validity. Reliability refers to the reproducibility of the results produced by the assessment; validity refers to whether a test measures what it purports to measure. Competency-based medical education (CBME) is an emerging concept in training that is being advocated as a substitute or, at least, a complement to the traditional time-based residencies. A white paper has been submitted to the Royal College of Physicians and Surgeons of Canada advocating a shift towards a CBME approach.9 In the United States, the Accreditation Council for Graduate Medical Education began an initiative in 1998 known as the outcome project; the program focuses on the competency domains.10 The Division of Orthopaedics at the University of Toronto and the Royal Australasian College of Psychiatry pilot projects are current examples of CBME.9 A successful shift towards a CBME approach requires three critical components: (1) identifying the required abilities; (2) identifying ways to teach the required abilities; and (3) identifying ways to assess these abilities. It is obvious that, as a specialty, we have a lot of work on these three fronts to move into a 21st century model of training.
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 | 0,007 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,064 | 0,014 |
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 ».