Online advertising and marketing claims by providers of proton beam therapy: are they guideline-based?
Bibliographic record
Abstract
BACKGROUND: Cancer patients frequently search the Internet for treatment options, and hospital websites are seen as reliable sources of knowledge. Guidelines support the use of proton radiotherapy in specific disease sites or on clinical trials. This study aims to evaluate direct-to-consumer advertising content and claims made by proton therapy centre (PTC) websites worldwide. METHODS: Operational PTC websites in English were identified through the Particle Therapy Co-Operative Group website. Data abstraction of website content was performed independently by two investigators. Eight international guidelines were consulted to determine guideline-based indications for proton radiotherapy. Univariate and multivariate logistic regression models were used to determine the characteristics of PTC websites that indicated proton radiotherapy offered greater disease control or cure rates. RESULTS: Forty-eight PTCs with 46 English websites were identified. 60·9% of PTC websites claimed proton therapy provided improved disease control or cure. U.S. websites listed more indications than international websites (15·5 ± 5·4 vs. 10·4 ± 5·8, p = 0·004). The most common disease sites advertised were prostate (87·0%), head and neck (87·0%) and pediatrics (82·6%), all of which were indicated in least one international guideline. Several disease sites advertised were not present in any consensus guidelines, including pancreatobiliary (52·2%), breast (50·0%), and esophageal (43·5%) cancers. Multivariate analysis found increasing number of disease sites and claiming their centre was a local or regional leader in proton radiotherapy was associated with indicating proton radiotherapy offers greater disease control or cure. CONCLUSIONS: Information from PTC websites often differs from recommendations found in international consensus guidelines. As online marketing information may have significant influence on patient decision-making, alignment of such information with accepted guidelines and consensus opinion should be adopted by PTC providers.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".