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Enregistrement W2595763112 · doi:10.1097/brs.0000000000002021

Introduction of New Technologies in Orthopedic Surgery

2017· article· en· W2595763112 sur OpenAlexaff
Paul A. Anderson

Notice bibliographique

RevueSpine · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueSurgical Simulation and Training
Établissements canadiensObject Research Systems (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineOrthopedic surgeryOrthopedic ProceduresGeneral surgerySurgeryMedical physics

Résumé

récupéré en direct d'OpenAlex

New technology may be less invasive, may cause less collateral damage and faster recovery, and offers promise for better diagnostic and treatment modalities. Most new technologies when first distributed do not translate to better outcomes. The choice of a new technology should be based on a biological need and justified by an established physiological mechanism. Unfortunately, new technology is often developed for marketing advantage rather than to address a biological need. Food and Drug Administration is the main regulator for both pharmaceutical and medical devices in the United States; its goal is to establish safety and efficacy. The agency permits marketing of devices but does not actually approve them, and assigns risk as low (class 1), moderate (class 2), or high (class 3) (Table 1).TABLE 1: Food and Drug Administration Classification for Medical DevicesPreclinical mechanical testing is usually based on testing standards created by the American Society of Testing Materials and the International Standards Organization, which include static and dynamic testing to determine strength and fatigue properties of all components. For devices that allow motion, wear testing measures durability. Cell culture is often performed on wear material, and biological testing for spinal devices can include use of a laminectomy model by which wear debris is placed on the nerve and neurotoxicity is examined. This type of testing can lead to unexpected failure in vivo after approval. Testing of input parameters versus what actually happens in vivo is not understood, and correlation between testing standards and clinical results is often inadequate. Clinical trials are most important for confirming safety and efficacy. Study design is essential in choosing an appropriate control, whether nonoperative therapy or a competing device or technique. Time points appropriate for determining reasonable safety and efficacy must be selected. A randomized controlled trial is most appropriate when control efficacy is unknown, outcomes are poorly measured, cost of treatment is high, and acceptable alternatives are available. A randomized controlled trial yields evidence of the highest quality and can prove efficacy but cannot show efficiency of treatment. Efficiency involves broadened indications, greater surgeon variability, and oftentimes less than optimal training and inconsistent management before and after surgery. These problems can greatly influence the distributive nature of new technology. Examples of successful new technology for spine surgery include cervical disc arthroplasty performed to address a biological need, at least theoretically, to prevent adjacent segment degeneration. An excellent alternative is fusion. A less successful example is a minimally invasive shaver used in foraminotomy. This technique involves making a small laminotomy and passing a guidewire out the neuroforamen and back through the skin, allowing placement of a shaver that can be used to undercut the foramina and the subarticular area of the lamina; this is done under special neuromonitoring. This device was approved as an instrument rather than a medical device because it is removed at the time of surgery. Systematic problems, including poor indications, inadequate physician training, lack of regulatory oversight, faulty surgeon understanding of neuromonitoring principles, and unproven safety and effectiveness, may lead to complications. The final aspect of new technology is surgeon education. Many experienced surgeons see new technology as not very different from what they are already doing and may not adequately prepare for its use. Courses typically involve cadaveric or synthetic models and may not include critical components, such as use of neuromonitoring. Surgeons using new technology should gain experience by visiting proficient users or by having their cases proctored. Assisting at their own hospital or at another surgeon's institution may provide the best training. By using the instruments in a sham process, that is, by doing a familiar similar technique while using instruments needed to place the new device or apply the new technique, the surgeon can improve skills. Through minimally invasive placement of pedicle screws by an open procedure, the surgeon can use percutaneous instruments. Early cases should be proctored and critically reviewed by the surgeon; CT scans should confirm that desired results were achieved. Learning must be contemporary with performance of the surgical procedure; if time passes between undergoing training and doing the procedure, the surgeon should retrain to keep skills up-to-date. To overcome the learning curve, the surgeon usually will need to perform complex tasks, such as completing an entirely new procedure, in 30 to 35 cases, and will require as few as 10 cases to add a new step to a procedure. Nandyala et al1 reviewed minimally invasive transforaminal lumbar interbody fusion and found a significant decrease in time, estimated blood loss, and IV fluids after the first 30 cases but no differences in rates of complications. Park et al2 noted a 9% complication rate and reported that most complications occurred in the first 40 cases. Another concept involves mass learning versus distributed learning. Mass learning is achieved in a single session, such as a half-day program, whereas distributed learning takes place in 1 hour spread over 4 days. Distributed learning leads to significantly longer-term retention of technical skills.3 In summary, new technology offers the promise of improved clinical outcomes but must be based on biological plausibility and a biological need. The surgeon cannot assume that testing is rigorous, and should evaluate the literature himself before adopting a new technology. The surgeon's training must be completed by practicing with high-fidelity models or by visiting surgeons experienced in the technique.

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)0,006
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,026
Score d'incertitude au seuil0,086

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,010
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0010,005
Communication savante0,0040,007
Science ouverte0,0010,004
Intégrité de la recherche0,0040,009
Charge utile insuffisante (le modèle a refusé de juger)0,0260,012

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 Opus0,052
Tête enseignante GPT0,328
Écart entre enseignants0,277 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

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
Publié2017
Routes d'admission1
Résumé présentoui

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