Unintended consequences of an ‘all-clear’ diagnosis for potential cancer symptoms: a nested qualitative interview study with primary care patients
Bibliographic record
Abstract
BACKGROUND: Nine out of 10 patients undergoing urgent cancer investigations receive an 'all-clear' diagnosis. AIM: A qualitative approach was used to evaluate the impact of investigations that did not result in cancer diagnosis on subsequent symptom attribution and help seeking for recurrent or new possible cancer symptoms. DESIGN AND SETTING: A survey of symptoms, help seeking, and past investigations was sent to 4913 individuals aged ≥50 years from four UK general practices. Of 2042 responders, 62 participants were recruited still reporting at least one cancer 'alarm' symptom in a 3-month follow-up survey for a nested in-depth interview study (ensuring variation in sociodemographic characteristics). METHOD: Framework analysis was used to examine the in-depth semi-structured interviews and identify themes related to previous health investigations. RESULTS: Interviewees were on average 65 years old, and 90% reported investigations within the previous 2 years. Most often they reported gastrointestinal, urinary, and respiratory symptoms, and 42% had waited ≥3 months before help seeking. Reassurance from a previous non-cancer diagnosis explained delays in help seeking even if symptoms persisted or new symptoms developed months or years later. Others were worried about appearing hypochondriacal or that they would not be taken seriously if they returned to the doctor. CONCLUSION: An all-clear diagnosis can influence help seeking for months or even years in case of new or recurrent alarm symptoms. Considering the increasing number of people undergoing investigations and receiving an all-clear, it is paramount to limit unintended consequences by providing appropriate information and support. Specific issues are identified that could be addressed.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".