From Social Media to Peer Review: How Can we Evaluate Medical Content for Misinformation and Bias?
Bibliographic record
Abstract
Traditionally, ophthalmologists stay current by referring to peer reviewed papers found on scientific databases, such as PubMed, where rigorous publication standards reduce the potential for bias. We now access medical information from diverse online sources and social media allowing for fast-paced dissemination of content. Access to this rapidly evolving online information has allowed us to be more versed in our specialized knowledge than ever before. However, the rise of social media use in medicine may challenge the traditional methods aimed to limit misinformation and bias. How can we identify and evaluate bias when we access information from multiple disparate online sources in 2023?
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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 | 0.633 | 0.918 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.039 | 0.026 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.045 | 0.042 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".