Web-Based Information on Spinal Cord Stimulation: Qualitative Assessment of Publicly Accessible Online Resources
Bibliographic record
Abstract
BACKGROUND: Despite the growing accessibility of web-based information related to spinal cord stimulation (SCS), the content and quality of commonly encountered websites remain unknown. OBJECTIVE: This study aimed to assess the content and quality of web-based information on SCS. METHODS: This qualitative study was prospectively registered in Open Science Framework. Google Trends was used to identify the top trending, SCS-related search queries from 2012 to 2022. Top queried terms were then entered into separate search engines. Information found on websites within the first 2 pages of results was extracted and assessed for quality using the DISCERN instrument, the Journal of the American Medical Association benchmark criteria, and the Health on the Net Foundation code of conduct certification. Website readability and SCS-related information were also assessed. RESULTS: After exclusions, 42 unique sites were identified (scientific resources: n=6, nonprofit: n=12, for-profit: n=20, news or media: n=2, and personal or blog: n=2). Overall, information quality was moderate (DISCERN). Few sites met all the Journal of the American Medical Association benchmark criteria (n=3, 7%) or had Health on the Net Foundation certification (n=7, 16%). On average, information was difficult to read, requiring a 9th- to 10th-grade level of reading comprehension. Sites described SCS subcategories (n=14, 33%), indications (n=38, 90%), contraindications (n=14, 33%), side effects or risks (n=28, 66%), device considerations (n=25, 59%), follow-up (n=22, 52%), expected outcomes (n=31, 73%), provided authorship details (n=20, 47%), and publication dates (n=19, 45%). The proportion of for-profit sites reporting authorship information was comparatively less than other site types (n=3, 15%). Almost all sites focused on surgically implanted SCS (n=37, 88%). On average, nonprofit sites contained the greatest number of peer-reviewed reference citations (n=6, 50%). For-profit sites showed the highest proportion of physician or clinical referrals among site types (n=17, 85%) indicating implicit bias (ie, auto-referral). CONCLUSIONS: Overall, our findings suggest the public may be exposed to incomplete or dated information from unidentifiable sources that could put consumers and patient groups at risk.
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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".