Do perceived barriers to clinical presentation affect anticipated time to presenting with cancer symptoms: an ICBP study
Bibliographic record
Abstract
Background: Cancer survival in the UK and Denmark are lower when compared with similar countries with late diagnosis a possible cause. We aimed to study the relationship between barriers to attending a primary care physician (GP) and anticipated time to help seeking (ATHS) with four cancer symptoms in six countries. Methods: A population-based survey measuring cancer awareness and beliefs conducted within the International Cancer Benchmarking Partnership in Australia, Canada, Denmark, Norway, Sweden and UK. Data were collected on perceived barriers to GP consultation (including embarrassment, worry about wasting the doctors' time, fear about what the doctor might find and being too busy) and ATHS for persistent cough, abdominal swelling, rectal bleeding and breast changes. Relationships between perceived barriers and ATHS were investigated using multivariable analysis. Results: Among 19 079 respondents, higher perceived barrier scores were associated with longer ATHS intervals for all symptoms studied (P < 0.01) responders with the highest barrier scores (>10.84) had between two and three times the odds of longer ATHS. ATHS was low in Australia for all symptoms and highest in Denmark for abdominal bloating. Conclusions: Perceived barriers to help-seeking have a role in delaying GP presentation. Early diagnosis campaigns should address emotional and practical barriers that reduce early presentation with potential cancer symptoms.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".