Re: McCannel: Simulation surgical teaching in ophthalmology (Ophthalmology 2015;122:2371-2)
Bibliographic record
Abstract
I commend Dr McCannel for his editorial,1McCannel C.A. Simulation surgical teaching in ophthalmology.Ophthalmology. 2015; 122: 2371-2372Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar highlighting the increasing importance of simulator learning in ophthalmology surgical training programs. With the development of advanced technology virtual reality simulators, trainees are now able to acquire significant technical skills before operating on patients. The incorporation of competency-based assessments into simulator training is another key factor worthy of mention. In 2013, our institution developed and implemented a formal surgical curriculum for ophthalmology residents using didactic teaching, wet laboratory training, and surgical simulation, with competency assessments required before transitioning to surgery. These formalized assessments ensure that trainees meet certain standards before progressing to more skilled levels involving patients, and also provide an incentive for the trainee to acquire simulator-based skills. Going forward, ophthalmology training programs also need to develop and incorporate formalized competency-based assessments of advanced trainees to ensure appropriate surgical skills are acquired before graduating to independent practice. The formalization of competency assessments into surgical training programs promises many potential benefits. It ensures we provide quality training to our future surgeons; it formalizes the notion that trainees are given a graded level of responsibility based on their skills, minimizing potential risk to patients; and it facilitates a more transparent discussion with our patients on the role trainees will play in their surgery. Simulation Surgical Teaching in OphthalmologyOphthalmologyVol. 122Issue 12Preview“Practice makes perfect” is an age-old idiom few can disagree with. Yet in ophthalmic surgical teaching, practice has not had the emphasis it has in other motor skill–based disciplines. Even a well-stocked wet laboratory likely does not have a large quantity of eyes available for practicing, limiting robustness of skill development that could be achieved by frequent repetition. The commonly used pig eyes have only superficially similar surgical characteristics to human eyes, reducing the benefit of the time invested. Full-Text PDF ReplyOphthalmologyVol. 123Issue 6PreviewDr McAlister appropriately points out that using the simulator to teach surgical skills is only part of the process of graduating competent surgeons. I agree that assessment of competency is also an important part of an optimal surgical training paradigm. Over the past decade or two, there has been an evolution of medical education that strongly favors formal skill assessment. This led to the adoption of the United States Medical Licensing Examination (USMLE) Step 2 Clinical Skills examination as a requirement for medical licensing in the United States in 2005. Full-Text PDF
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".