Thoracic Surgery Information on the Internet: A Multilingual Quality Assessment
Bibliographic record
Abstract
BACKGROUND: Previous data suggest that quality of Internet information regarding surgical conditions and their treatments is variable. However, no comprehensive analysis of website quality exists for thoracic surgery. OBJECTIVE: The aim of this study was to quantify website quality in a multilingual setting using an international standard for assessment. METHODS: Health On the Net (HON) principles may be applied to websites using an automated toolbar function. We used the English, French, Spanish, and German Google search engines to identify 12,000 websites using keywords related to thoracic conditions and procedures. The first 150 websites returned by each keyword in each language were examined. We compared website quality to assess for tertile (is the quality better in first, second, or third 50 websites returned) and language differences. A further analysis of the English site types was undertaken performing a comparative analysis of website provider types. RESULTS: Overall, there are a considerable number of websites devoted to thoracic surgery: "lung cancer" returned over 150 million websites. About 7.85% (940/11,967) of websites are HON-accredited with differences by search term (P<.001) and tertiles (P<.001) of the first 150 websites, but not between languages. Oncological keywords regarding conditions and procedures were found to return a higher percentage of HON-accreditation. The percentage of HON-accredited sites was similar across all four languages (P=.77). In general, the first tertile contained a higher percentage of HON-accredited sites for every keyword. CONCLUSIONS: Clinicians should appreciate the lack of validation of the majority of thoracic websites, with discrepancies in quality and number of websites across conditions and procedures. These differences appear similar regardless of language. An opportunity exists for clinicians to participate in the development of informative, ethical, and reliable health websites on the Internet and direct patients to them.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".