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Enregistrement W4405563089 · doi:10.2106/jbjs.24.01079

Robot-Assisted Arthroplasty Research Focuses on the Wrong Outcomes

2024· article· en· W4405563089 sur OpenAlexaff
Kim Madden, Anthony Adili

Notice bibliographique

RevueJournal of Bone and Joint Surgery · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueTotal Knee Arthroplasty Outcomes
Établissements canadiensMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Organismes subventionnairesnon disponible
Mots-clésArthroplastyRobotMedicineComputer scienceSurgeryArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

Commentary Robots have been used in arthroplasty for decades, but the technology is now substantially growing in popularity, making the article by Ekhtiari et al. a timely review that we read with interest. Their well-conducted review rightly points out that media reports of robot-assisted arthroplasty are more likely than scientific reports to report positive outcomes. Studies evaluating the precision of implant placement using robotic technology have been overwhelmingly positive, but studies assessing more patient-important outcomes such as time to recovery and postoperative range of motion, function, and pain reduction have been mixed. For example, Liow et al.1 found that patients who underwent robot-assisted total knee arthroplasty (TKA) had better function scores at 2 years postoperatively compared with those who underwent manual TKA, whereas Clement et al.2 found better pain scores but no difference in function. It may be the case that commonly used patient-reported outcome measures (PROMs) in arthroplasty do not adequately measure important improvements in function, and therefore functional differences between groups are not detected (e.g., due to ceiling effects, failure to measure what modern patients value, or inability to discriminate small differences)3,4. We also believe that these studies comparing PROMs between robot-assisted and manual TKA are not designed to assess a key rationale for the use of robots in arthroplasty: that robotic technology is an enabling technology that allows the surgeon to have access to more information and to personalize knee arthritis treatment with arthroplasty in a way that has never been possible before. These outcomes are difficult to evaluate with randomized controlled trials (RCTs), which may also account for the bias in media reporting toward more positive outcomes than in the scientific studies to date. The rich intraoperative data collected by modern robotic systems can be used in research to determine variables that have an impact on patient outcomes. For example, the raw intraoperative data from robotic systems, in conjunction with motion capture systems for gait analysis, can enable us to better understand biomechanics after arthroplasty in order to inform patient-specific personalized treatment plans and future research. Additionally, the robotic technology can be used as a novel teaching tool for trainees, as the extensive intraoperative evaluation and data available with this new technology aid in visualizing and understanding the variables that are under the direct control of the surgeon during the arthroplasty. The robotic technology allows for modeling various scenarios preoperatively and intraoperatively before committing to a final implant plan. Such capabilities, which are not available with manual techniques, allow surgeons and trainees to visualize and better understand how these parameters interact with each other, and their degree of codependency, in real time. Perhaps most importantly, the robotic technology has allowed surgeons to change their perspective on how TKA is performed. Surgeons can perform “à la carte” surgery in which they preoperatively plan for and model multiple scenarios, then intraoperatively choose the one that is the most appropriate based on the patient’s anatomy, visualization of the preoperative plans using the robotic software, and surgical judgment. The arthroplasty robot can be used to shift away from treatment of all grades of knee arthritis with TKA and toward utilizing unicompartmental and even bicompartmental knee arthroplasties whenever possible, with the patient’s anatomy and disease stage dictating the type of replacement employed. There is some evidence that surgeons who use robots in arthroplasty are more likely to consider individualized operative plans while surgeons who perform manual arthroplasty tend to choose a more standardized approach5, which could indicate that robotic technology opens new avenues for innovation in arthroplasty. In summary, Ekhtiari et al. conducted a high-quality systematic review on media portrayals versus available evidence in robot-assisted TKA, which they point out are mismatched. Although this is true, and caution is always required with emerging interventions, we need more primary studies that go beyond evaluating implant placement or whether surgeons can perform a better TKA with a robot. That is too simplistic and ignores the educational benefits, availability of rich data, and innovation benefits such as changing surgeon paradigms about how arthroplasty is performed. Robots are enabling tools that allow surgeons to hit their target precisely; now, the question we have to ask ourselves is “what target are we aiming for?”

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,025
score de la tête « metaresearch » (Gemma)0,198
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,975
Score d'incertitude au seuil0,132

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0250,198
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,003
Bibliométrie0,0040,003
Études des sciences et des technologies0,0020,005
Communication savante0,0050,006
Science ouverte0,0050,002
Intégrité de la recherche0,0180,017
Charge utile insuffisante (le modèle a refusé de juger)0,0120,006

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.

Tête enseignante Opus0,131
Tête enseignante GPT0,345
Écart entre enseignants0,214 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineÉvaluation
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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