Surgery 101 Podcast: Episodes 111–120
Bibliographic record
Abstract
Abstract Surgery 101 is a series of podcasts intended to serve as brief introductions or reviews of surgical topics for medical students. We aim to cover a single topic in 15–20 minutes so that learners can quickly grasp the basic concepts relating to a common surgical problem. Learning objectives are provided for each episode; episodes are divided into chapters and conclude with several key points to summarize the topic. Surgery 101 has been produced since October 2008; it was created by Dr. Parveen Boora and Dr. Jonathan White, and is currently produced by the Undergrad Surgery Mobile Podcasting Studio Team which is: Jonathan White, Nishan Sharma, Jenni Marshall, Katrina Pederson, Shannon Erichsen and Tracy Smereka, with the assistance of the members of the Department of Surgery at the University of Alberta. The Surgery 101 podcasts can be downloaded for free from the iTunes Music Store, from surgery101.org, from MedEdPORTAL and from the Canadian Healthcare Education Commons. As of March 2013, our episodes have been downloaded more than 850,000 times. Our experience with the Surgery 101 podcasts has been published in Medical Teacher. To date, 122 episodes have been released, and new episodes are added every Friday. Episodes 111–120 are included in this resource.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.396 | 0.148 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".