Endoscopic capacity in West Africa
Bibliographic record
Abstract
BACKGROUND: Levels of endoscopic demand and capacity in West Africa are unclear. OBJECTIVES: This paper aims to: 1. describe the current labor and endoscopic capacity, 2. quantify the impact of a mixed-methods endoscopy course on healthcare professionals in West Africa, and 3. quantify the types of diagnoses encountered. METHODS: In a three-day course, healthcare professionals were surveyed on endoscopic resources and capacity and were taught through active observation of live cases, case discussion, simulator experience and didactics. Before and after didactics, multiple-choice exams as well as questionnaires were administered to assess for course efficacy. Also, a case series of 23 patients needing upper GI endoscopy was done. RESULTS: In surveying physicians, less than half had resources to perform an EGD and none could perform an ERCP, while waiting time for emergency endoscopy in urban populations was at least one day. In assessing improvement in medical knowledge among participants after didactics, objective data paired with subjective responses was more useful than either alone. Of 23 patients who received endoscopy, 7 required endoscopic intervention with 6 having gastric or esophageal varices. Currently the endoscopic capacity in West Africa is not sufficient. A formal GI course with simulation and didactics improves gastrointestinal knowledge amongst participants.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".