A critical analysis of online patient‐directed resources on catheter ablation for ventricular arrhythmias
Bibliographic record
Abstract
Background: Patients search online content to improve their understanding of medical procedures. The quality of online patient education materials (OPEMs) on catheter ablation of ventricular arrhythmias (VAs) requires investigation. Methods: Six predetermined search terms relating to VA ablation were used to search Google, Bing and Yahoo for written OPEMs, and YouTube for video OPEMs. Written OPEMs were assessed for readability using five readability indices to produce a required reading grade level, and quality using the DISCERN and Journal of the American Medical Association (JAMA) instruments. Video OPEMs were assessed for quality according to compliance with a list of investigator-developed essential discussion points. Results: 1200 written and 480 videos were identified using the search strategy, of which 60 and 25 respectively were unique OPEMs included in this study. The mean reading grade level for written OPEMs was 11.3 ± 1.9, with no articles being written at the 6th grade level recommended by the American Medical Association. Using quality metrics, only 26.7% of written OPEMs attained a 'high-quality' JAMA rating, and 30% had a DISCERN score of 'good' or better. Video OPEMs similarly had poor quality, only discussing a mean of 3.50 ± 2.57 out of 18 total essential criteria. Conclusion: There is a paucity of online patient-directed materials on VA catheter ablation. Available OPEMs are of insufficient quality to adequately convey essential information, and written OPEMs are written at a level higher than the recommended reading level.
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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 | 0.053 | 0.391 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.043 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".