P.081 Popularity of online multimedia educational resources in neurosurgery: Insights from The Neurosurgical Atlas project
Bibliographic record
Abstract
Background:The Neurosurgical Atlas is a neurosurgical website with informative chapters and videos to promote excellence and safety in neurosurgical techniques. Here, we present our analysis of this website’s viewing data and describe how online neurosurgical resources are being utilized. We hope this will be a useful guide for neurosurgeons interested in online multimedia education. Methods: We analyzed Google Analytics data from The Neurosurgical Atlas between June 2016 and August 2017 which tracked user demographics, geographical location, and the videos watched. Views were also analyzed categorically by dividing videos into six neurosurgical topics and into basic and advanced levels as per their surgical complexity. Results: There were 246,259 website visits and 143,868 video plays. The most frequent age groups were 25-34 (44%) and 35-44 (24%). 71% of visitors were male. Most visitors were from the US (29.52%) and Brazil (6.43%). Website visits and video plays increased over time, with multiple peaks corresponding to promotional email updates. The six neurosurgical topics were all similarly popular. Conclusions: Our study presents the first piece of evidence demonstrating the feasibility and popularity of a free online resource in neurosurgical education. Our experience highlights the growing demand for free-access online chapters, anatomical illustrations, and operative videos.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".