Upper Gastrointestinal Cancer: Delays in Diagnosis and Treatment Caused by Barriers to Healthcare in the Latino Community
Bibliographic record
Abstract
We report a case of an 81-year-old male immigrant from a Latin American developing country with a high burden of upper gastrointestinal neoplasms, who presented with a small bowel gastrointestinal stromal tumor (GIST) after 2 years of delay in the diagnosis due to multiple barriers to healthcare. The patient presented with a partial intestinal obstruction in an abdominal computed tomography (CT) scan suggestive of a GIST. Surgical resection was performed, and adjuvant therapy was initiated with imatinib (a tyrosine kinase inhibitor) after the diagnosis was confirmed. The patient had a successful outcome. Due to his migratory status, the patient planned to follow up with different health providers in two different countries, which constitutes a common challenge in the immigrant population.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".