Quality of Online Information Regarding Cervical Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction The internet is an important source of health information, and yet the quality of the resources that patients' access can vary widely. Previous research has evaluated the quality of information for several types of cancer; however, this has not yet been done for cervical cancer beyond treatment information. The goal of this project was to systematically evaluate the quality of resources for cervical cancer information available against a range of metrics, including content breadth and accuracy, readability, and accountability. Methods An internet search was performed using the term "cervical cancer" using Google and two meta-search engines, Dogpile and Yippy. The top-100 websites returned across all three engines were evaluated using a validated structured rating tool. Results Only 32% of websites disclosed their author and only 38% used citations, while 64% of websites had been updated in the last two years. Readability was at university-level or higher for 19% of websites, and high-school level for 78%. Coverage was highest for etiology and risk factors (93% of websites) and prevention strategies such as pap smears and vaccines (92%); coverage was lowest for prognosis (49%), staging (52%), side effects (47%), and follow-up (25%). When a topic was covered the information was predominantly accurate, and few websites had inaccurate information. At least one social-media platform was linked to by 79% of websites. Conclusions This project highlights the strengths and limitations in the quality of the top-100 informational cervical cancer websites. These findings can inform the dialogue between health care providers and patients around selecting and evaluating information resources. These findings can also inform specific improvements to make online resources for cervical cancer more accessible, comprehensive, and relevant to patients.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it