Developing a Video-Based Learning Module to Teach Decision-Making for Cubital Tunnel Surgery
Bibliographic record
Abstract
BACKGROUND: Although intraoperative educational videos have become increasingly popular, comparatively few videos teach clinical reasoning for surgical procedures. The objectives of this study were to develop an engaging online video-based module to teach decision-making for cubital tunnel surgery, including supercharge nerve transfer, using a multimedia learning framework; and evaluate its effectiveness and use for continuing professional development. METHODS: The educational module consisted of a prelecture knowledge assessment, choice of two self-guided video lectures (7 minutes and 28 minutes), and a postlecture knowledge assessment. An additional assessment examined knowledge retention 3 months after module completion. Surgeon surveys were administered after each knowledge assessment. RESULTS: A total of 279 surgeons participated in the educational module (75 percent practicing surgeons, 25 percent trainees), 112 surgeons completed the postlecture assessment, and 71 surgeons completed the knowledge retention assessment. Median score on the prelecture assessment was five out of 10 (interquartile range, four to seven). Scores improved by three points (10-point scale; p < 0.0001) in the postlecture assessment. Median score on the knowledge retention assessment was eight out of 10 (interquartile range, six to nine), with participants maintaining a two-point increase from their prelecture score ( p = 0.0002). Among surgeons completing this assessment, 68 percent reported that the module had changed their management of cubital tunnel syndrome. CONCLUSIONS: This study introduces a framework for the development of online multimedia modules for surgical education. It also underscores a demand among surgeons for easily accessible, reusable educational resources. Similar video-based modules may be developed to address this demand to facilitate continuing professional development in surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".