Reasons for delays in diagnosis of anal cancer and the effect on patient satisfaction.
Bibliographic record
Abstract
OBJECTIVE: To quantify the time to diagnosis of anal cancer after onset of symptoms, to identify reasons for delays in diagnosis, and to identify the effect of delays on patient satisfaction. DESIGN: Retrospective questionnaire. SETTING: Cross Cancer Institute in Edmonton, Alta. PARTICIPANTS: Patients newly diagnosed with anal cancer on their first visit to the centre. MAIN OUTCOME MEASURES: Timeline from first symptoms to first access to medical care and to diagnosis, and patient satisfaction. RESULTS: Twenty-six patients completed the survey. Although most sought medical attention promptly, 19% waited for more than 6 months. At first visits after symptom onset, a rectal examination was performed in only 54% of patients, a diagnosis of hemorrhoids was given in 27% of patients, and further investigations were ordered in only 54% of patients. If a misdiagnosis of hemorrhoids was made, substantially more visits were required to diagnose the cancer. An average of 3.2 months after the first visit to a physician and 7.4 months after onset of symptoms was needed to obtain a diagnosis. Overall, 28% of patients believed there were no diagnostic delays and 40% of patients thought they were responsible for the delay. Overall, 72% of patients were satisfied with the care they received. Patients who were dissatisfied perceived the delay in diagnosis to be because no action was taken by a physician or the wait was too long for tests or referrals. CONCLUSION: To reduce delays in diagnosis, it might be important to educate relevant populations about symptoms of anal cancer. In addition, primary care physicians must maintain a high index of suspicion of anal cancer in high-risk populations. Finally, there must be a system-wide increase in access to further investigations through gastroenterologists and general surgeons.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".