205 Bridging the Gap: Identifying Barriers to Enable the Integration of Tobacco Cessation Into Cancer Patient Care
Bibliographic record
Abstract
CARO-ASM 2019 participants (97%) used the Internet, and 87% had searched for thyroid cancer information.The majority (94%) used the search engine Google.Patients most often looked for information about treatment (94%) and symptom management (76%).Patients most often read websites from non-profit organizations (62%), academic or healthcare institutions (48%), or commercial websites (41%).Patients evaluated content quality by comparing several resources (71%), discussing with a physician (56%) or using a credible academic or government site (53%).Online information was somewhat hard to understand for 32% of respondents, but 91% found it useful.More than half (60%) of patients reported that treatment decisions were affected by web resources, and information helped 50% of patients make decisions with their physicians.Respondents highlighted a lack of resources on survivorship and less common tumours such as medullary or anaplastic cancer.Conclusions: This is the first study to examine thyroid cancer patients' Internet use.Clinicians should recognize that patients overwhelmingly access online information that often impacts their treatment decision-making.Many patients do not simply select the first search results, and actively assess website quality.However, there are gaps between what information patients seek, and what they find.Clinicians can play a key role in guiding thyroid cancer patients through the abundance of web-based information and assisting them in interpretation.Educators can also use this information to guide resource development, tailoring content and design to thyroid cancer patients' needs.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".