Content Assessment of Surgical-Education Videos on YouTube for Total Laparoscopic Hysterectomy
Bibliographic record
Abstract
Abstract Objective: This study assessed the content of online videos as educational supplements for gynecology trainees for total laparoscopic hysterectomy (TLH). Materials and Methods: A cross-sectional analysis was performed of relevant YouTube videos. The key words laparoscopic total hysterectomy surgical training and laparoscopic total hysterectomy surgical education were used to retrieve the top 60 videos per search query. Videos without live surgical footage, audio, or written narration; or with commercial intent were excluded. Two independent reviewers evaluated the videos, using a modified version of the Objective Scale Specific for the Assessment of Technical Skills for Laparoscopic Hysterectomy (H-OSATS) to assess inclusion of necessary steps; the Objective Structured Assessment of Technical Skills (OSATS) for laparoscopy to assess surgical technique; and a 3-point Likert scale to assess surgical difficulty. Results: A total of 12 videos were analyzed: 11 (91.6%) videos were created by physicians without academic affiliations and 1 was from an academic center. The mean H-OSATS and OSATS scores were 21.75/53 (41%) and 16.05/20 (82.5%), respectively. Inter-rater agreement was rated at 95.3% with a Cohen's κ of 0.90. All videos included appropriate instruction on division of the major uterine blood supply (100%). Conversely, very few videos included information about preoperative instruction for patient positioning (5.56%), abdominal entry (12.50%), trocar insertion (14.58%), and inspection of the peritoneal cavity (20.83%). Mean surgical difficulty was rated as easy (1.6/3). Conclusions: Online surgical videos demonstrate adequate surgical skill and technique, but do not provide thorough inclusion of the steps required to perform TLH. There is a need to produce open-access high-quality comprehensive videos for TLH instruction. (J GYNECOL SURG 39:190)
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".