Bibliographic record
Abstract
e all do it.With the click of a mouse, tap of a tablet screen or touch of a smart phone, we access information.We do it to shop, to learn of current events and to keep in contact with friends and colleagues.In the clinic and the operating room, teachers and learners access health information daily.Patients and families routinely arrive in clinic requesting a second opinion after they've already had a private consultation with Dr. Google.How reliable is health information on the Internet?Six years ago we published on the veracity of online information available regarding cryptorchidism. 1 Of 124 websites, only 35% were endorsed by a non-profit accrediting body, 77% did not provide references for the information provided and 48% did not identify an author for the content.Multivariate analysis showed that only accreditation status was associated with high quality content.At that time, a 35% accreditation rate was an improvement compared to previous assessments of the content validity of urological websites.2,3 We predicted that accreditation rates would continue to rise as the Internet and its users matured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.857 | 0.806 |
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".