What are patients reading? Assessing the quality of online resources on sudden cardiac arrest in athletes
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients frequently use the internet as an educational resource and sometimes in complete substitution of an expert physician’s consultation. Sudden cardiac arrests (SCA) in athletes are often sensationalised and attract much online readership. No studies have explored the quality of online content relating to SCA in athletes. Purpose To assess the quality of online educational resources on SCA in athletes. Methods The first 200 hits of the search "sudden cardiac arrest in athletes" on Google were analyzed. For each website, reading difficulty was evaluated using 5 validated reading indices. Content quality was assessed with point-based scoring systems established by expert opinion. Results A total of 115 unique web domains met eligibility criteria, only 2 of which were within the limits of national recommended readability level (≤8th grade). 53.9% were from educational institutions. Mean content accuracy scored only 6.5 ± 3.9 out of 15 (43%), and mean school-grade reading level was 13.3 ± 2.9 years (equivalent to a post-secondary level of education). The majority of websites had poor lay appropriate explanations of concepts; 91.3% failed to adequately explain basic principles of treatment; 68.7% failed to mention over half of common etiologies associated with SCA; only 59.1% correctly cited incidence of SCA amongst young athletes; 24.3% did not explore management options and only 28.7% mentioned treatment options beyond immediate resuscitation. Higher reading difficulty correlated with higher accuracy scores (β 1.1, p < 0.001). Conclusion There is a paucity of accurate, lay language appropriate online health information on SCA in athletes. Incomprehensible and incomplete information may lead to inappropriate decisions by patients. A collaborative approach between experts and patients may assist in the creation of resources related to athletes’ health.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".