Delay in diagnosis and treatment of gastrointestinal cancer in Nepal.
Bibliographic record
Abstract
e18269 Background: Gastrointestinal (GI) cancers represent a major health challenge worldwide including Nepal where patients (pts) often present with advanced disease. The purpose of this study was to determine the time delay in diagnosis and treatment by evaluating time from first symptoms to diagnosis and treatment and to identify contributing factors from both pts and the health system in Nepal. Methods: An IRB approved cross sectional study was performed in pts with GI cancers. 50 newly diagnosed pts were enrolled and interviewed with a standardized questionnaire during the last 6 months of 2018. Diagnosis delay was defined as time from first symptoms to histopathological diagnosis. Treatment delay was defined as time from diagnosis to surgery and/or treatment by medical/ radiation oncologist. Results: The median age at diagnosis was 52.5 years. 52% had gastroesophageal cancer and 48% had colorectal cancer. 84% presented with Stage III/ IV disease. The median diagnosis delay was 217 days and the median treatment delay was 37 days. The median patient delay (time from first symptoms to first medical consultation) was 150 days. 64% were illiterate, 94% had a history of self medication prior to first medical consultation, 68% were from rural areas with limited healthcare facilities and 72% were unaware of causes of GI cancers. Reasons for diagnostic delay appear to be self diagnosis, self medication and lack of a prompt referral system. Reasons for treatment delay included financial constraint, prolonged wait times for procedures and treatment due to limited skilled manpower. Conclusions: Our data shows there is a significant delay in diagnosis and treatment especially in the time from first symptoms to first medical consultation. We found many preventable reasons for this that, if addressed appropriately, could have a significant impact on reducing the morbidity and mortality of GI cancers. There is an urgent need for intensive and comprehensive cancer education in Nepal and other developing countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".