Exploring why European primary care physicians sometimes do not think of, or act on, a possible cancer diagnosis. A qualitative study
Bibliographic record
Abstract
BACKGROUND: While primary care physicians (PCPs) play a key role in cancer detection, they can find cancer diagnosis challenging, and some patients have considerable delays between presentation and onward referral. AIM: To explore European PCPs' experiences and views on cases where they considered that they had been slow to think of, or act on, a possible cancer diagnosis. DESIGN & SETTING: A multicentre European qualitative study, based on an online survey with open-ended questions, asking PCPs for their narratives about cases when they had missed a diagnosis of cancer. METHOD: Using maximum variation sampling, PCPs in 23 European countries were asked to describe what happened in a case where they were slow to think of a cancer diagnosis, and for their views on why it happened. Thematic analysis was used to analyse the data. RESULTS: A total of 158 PCPs completed the questionnaire. The main themes were as follows: patients' descriptions did not suggest cancer; distracting factors reduced PCPs' cancer suspicions; patients' hesitancy delayed the diagnosis; system factors not facilitating timely diagnosis; PCPs felt that they had acted wrongly; and problems with communicating adequately. CONCLUSION: The study identified six overarching themes that need to be addressed. Doing so should reduce morbidity and mortality in the small proportion of patients who have a significant, avoidable delay in their cancer diagnosis. The 'Swiss cheese' model of accident causation showed how the themes related to each other.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".