Learning curves for cardiothoracic and vascular surgical procedures – a systematic review

Arora, Karan Singh; Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Ahmed, Kamran

doi:10.1080/00325481.2014.996113

Cited by 39 publications

(16 citation statements)

References 50 publications

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“…The learning curve of cardiothoracic and vascular surgical procedures has been summarized in a total of 48 studies, as Arora et al reported in their systematic review. 38 Based on operating time, the learning curve for coronary artery bypass surgery ranged between 15 and 100 cases; for endoscopic vessel harvesting and other cardiac vessel surgery, between 7 and 35 cases; for valvular surgery, which included repair and replacement, between 20 and 135 cases; for video-assisted thoracoscopic surgery, between 15 and 35 cases; for vascular neurosurgical procedures, between 100 and 500 cases, based on complications; for endovascular vessel repairs, between 5 and 40 cases; and for ablation procedures, between 25 and 60 cases. The authors concluded that the learning curves for cardiothoracic and vascular procedures varied depending on the procedure and the level of experience of the clinician.…”

Section: Resultsmentioning

confidence: 99%

Implant Placement Accuracy Using Dynamic Navigation

Block¹,

Emery²,

Lank³

et al. 2017

Int J Oral Maxillofac Implants

189

168

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The aim of this prospective study was to determine platform and angle accuracy for dental implants using dynamic navigation, a form of computer-assisted surgery. Three hypotheses were considered: (1) the overall accuracy for implant placement relative to the virtual plan is similar to that of static tooth-borne computerized tomography (CT)-generated guides; (2) the dynamic system is more accurate than freehand methods; and (3) there is a learning curve associated with this method. Materials and Methods: This study involved three surgeons placing implants in the mandible and maxilla of patients using a dynamic navigation system (X-Guide, X-Nav Technologies). Virtual implants were placed into planned sites using the navigation system computer. Post-implant placement cone beam CT scans were taken on all patients. For each patient, this scan was mesh overlayed with the virtual plan and used to determine platform and angular deviations to the virtual plan. The primary outcome variables were platform and angular deviations comparing the actual placement to the virtual plan. Secondary analyses included determination of accuracy related to case experience and freehand placement of implants. Comparisons to published accuracy studies were made for implant placement using static guides. Results: Accuracy deviations from the virtual plan were similar to those reported for static tooth-based guides using literature references as the comparison. The accuracy of dynamic navigation was superior compared to freehand implant placement. The three surgeons had similar accuracies after their learning curve was achieved. Proficiency based on case series was achieved by the 20th surgical procedure. Conclusion: Dynamic navigation can achieve accuracy of implant placement similar to static guides and is an improvement over freehand implant placement. In addition, there was a learning curve to achieve proficiency. Int J Oral MaxIllOfac IMplants 2016 (8 pages).

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Section: Resultsmentioning

confidence: 99%

Implant Placement Accuracy Using Dynamic Navigation

Block¹,

Emery²,

Lank³

et al. 2017

Int J Oral Maxillofac Implants

189

168

View full text Add to dashboard Cite

show abstract

“…The learning curve of for multi-port VATS has been described as variable and vague in previous publications (13,14). To minimize the potential bias to the study, the surgical videos of the uniportal VATS lobectomy were reviewed to determine the procedure-specific duration, which was recorded from the beginning of hilum dissection until removal of the specimen, excluding the time for thoracoscopic adhesiolysis and mediastinal lymph node dissection, which was unpredictable and varied significantly from procedure to procedure (15).…”

Section: Discussionmentioning

confidence: 99%

Learning curve for uniportal video-assisted thoracoscopic surgery lobectomy—results from 120 consecutive patients

Liu¹,

Chen²,

Shen³

et al. 2018

J. Thorac. Dis.

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“…Trainees in microsurgery find themselves faced with a steep learning curve to acquire and develop their operative skills. The surgical learning curve is an improvement in trainee-surgeon's performance over time as a result of new motor skills acquisition (Arora et al 2015). Technical expertize is broadly proportional to overall experience, and early exposure with appropriate high-quality training and repeated practice is essential (Sadideen & Kneebone 2012).…”

Section: Introductionmentioning

confidence: 99%

Twelve tips for postgraduate or undergraduate medics building a basic microsurgery simulation training course

et al. 2016

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Microsurgery is used in a variety of surgical specialties, including Plastic Surgery, Maxillofacial Surgery, Ophthalmic Surgery, Otolaryngology and Neurosurgery. It is considered one of the most technically challenging fields of surgery. Microsurgical skills demand fine, precise and controlled movements, and microsurgical skill acquisition has a steep initial learning curve. Microsurgical simulation provides a safe environment for skill acquisition before operating clinically. The traditional starting point for anyone wanting to pursue microsurgery is a basic simulation training course. We present twelve tips for postgraduate and undergraduate medics on how to set up and run a basic ex-vivo microsurgery simulation training course suitable for their peers.

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Learning curves for cardiothoracic and vascular surgical procedures – a systematic review

Cited by 39 publications

References 50 publications

Implant Placement Accuracy Using Dynamic Navigation

Implant Placement Accuracy Using Dynamic Navigation

Learning curve for uniportal video-assisted thoracoscopic surgery lobectomy—results from 120 consecutive patients

Twelve tips for postgraduate or undergraduate medics building a basic microsurgery simulation training course

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