The Past and Present of Breast Cancer Resources: A Re-evaluation of the Quality of Online Resources After Eight Years
Bibliographic record
Abstract
Background and objective The internet has become a major resource of information for cancer patients. However, the quality of these resources is variable, and a better understanding is needed to guide physicians as to how to best support patients in their online searches. We previously evaluated the quality of online breast cancer resources in 2011. Nearly a decade later, we aimed to assess the present quality of online breast cancer-related information and to compare our current analysis with data collected in 2011. Methods A list of 100 breast cancer websites was systematically compiled using meta-search engines Yippy and Dogpile and the search engine Google using the search term "breast cancer". Content accuracy and quality markers, including authorship, attribu-tion, currency, site organization, and readability were assessed by using a previously validated standardized rating tool. Results were analyzed using descriptive statistics and Fisher's exact test. The same strategy was used in both 2011 and 2019. Results When comparing 2011 data to the current one, 27% of websites had been updated in the previous two years in 2011 compared to 65% in 2019 (p<0.00001). Both data sets remained similar in terms of website disclosures and objectivity. Only 30% of websites analyzed in 2019 used two or more reliable sources, while 63% had no reliable sources or no sources cited. From 2011 to 2019, resources with readability above grade 12 increased from 4% to 30% (p<0.0001), while websites offering educational support rose from 8% to 35% (p<0.0001). In 2019, treatment and etiology/risk factors were the most accurately covered areas (64% and 63% of websites, respectively). In 2011, 63% of websites were found to be globally accurate. Prognosis coverage increased from 18% to 33% from 2011 to 2019 (p=0.02). In 2019, survivorship was also evaluated and found to be covered in only 24% of resources. Conclusion Over the past eight years, there have been variable changes in the quality of online breast cancer resources. Promisingly, websites are being updated more frequently and the educational support offered is expanding. Furthermore, there has been significant improvement in the coverage of prognosis, although this requires further progress. Unfortunately, websites are becoming increasingly challenging to understand for the average patient, and coverage of survivorship is lacking. Our study provides vital information to healthcare providers on these trends in online breast cancer resources and how to best support patients in their internet searches.
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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 | 0.021 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".