Reliance on Self-Medication Increase Delays in Diagnosis and Management of GI Cancers: Results From Nepal
Bibliographic record
Abstract
PURPOSE Patients with GI cancers in Nepal often present with advanced disease and poor outcomes. The purpose of the study was to determine the time to presentation, diagnosis, and treatment of GI cancer and the baseline factors that may be associated with delays. PATIENTS AND METHODS An institutional review board–approved study was performed in Kathmandu, Nepal, from July 2018 to June 2019. Patients with newly diagnosed GI cancers were asked to fill out a standardized questionnaire. Baseline factors such as residence, literacy, and use of self-medication were recorded. Patients were asked to report the time from first symptom to presentation, time from primary care visit to pathologic diagnosis, and time from diagnosis to surgery and/or treatment. Baseline factors were analyzed using 2-tailed t tests (Prism 8.0; GraphPad, La Jolla, CA) to determine whether any factors were associated with longer time delays in these 3 intervals. RESULTS The cohort comprised of 104 patients with a median age of 53.5 years (range, 22-77 years); 61.5% were men, 46.2% had upper GI cancers, and 83.7% presented with stage III or IV disease. The median time to presentation was 150 days, time to diagnosis was 220 days, and time to treatment was 50 days. There was no statistically significant difference in time intervals between upper and lower GI cancers. Use of self-medication (88.5%) was the only factor associated with longer time intervals to presentation, diagnosis, and treatment. CONCLUSION Patients in Nepal have long time intervals to presentation, diagnosis, and treatment of GI cancer. Self-medication led to longer delays. Reasons for self-medication and other potential barriers will be explored in future studies in the hopes of improving outcomes.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 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".