The Quality of Online Information for an Uncommon Malignancy—Neuroendocrine Tumours (NETs)
Bibliographic record
Abstract
BACKGROUND: Patient information is critical in shared decision-making and patient-centred management for neuroendocrine tumours (NETs). Most adults search the internet for health issues, with over half considering such information to be credible. Therefore, we evaluated the quality of online information on NETs. METHODS: Searching for "Neuroendocrine Tumours", the top 20 websites from Google and top 10 from Yahoo and Bing were identified. Open-access websites written in English were included. Websites indicated as advertisements or directed towards healthcare providers were excluded. Each website was evaluated using the JAMA benchmarks, DISCERN instrument, and the Health on the Internet (HONCode) seal by two independent reviewers. RESULTS: We included 16 unique websites after removing duplicates. Four were education pages from healthcare institutions, 10 were Cancer Society pages, and 2 were general information pages. The average score for JAMA benchmarks was 2.3, with 19% of websites receiving the highest score of 4. Specifically, 31% met the benchmark for authorship, 69% for attribution, 94% for disclosure, and 44% for currency. The average score for the DISCERN instrument was 46.5, with no website achieving the maximum of 80 points. The HONCode seal was present in 3 out of 16 websites (18%). CONCLUSIONS: We identified major issues with the quality of online information for NETs using validated instruments. The majority of websites identified through common search engines are low-quality. Patients should be informed of the limited quality of online information on NETs. High-quality online information is needed to ensure that patients can avoid misinformation and actively participate in their care.
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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 | 0.036 | 0.225 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".