Methods and findings on an ophthalmic mission trip to colombia
Bibliographic record
Abstract
Background: Medical ministry international (MMI) is a Canadian charitable organization that coordinates healthcare missions to underserved people in developing countries.Objective: We report on the baseline clinical findings of a mission trip to Ciénaga de Oro, Colombia, present the methods used in the mission and the efforts to establish a local eye care service with long-term sustainability.Methods: Data were collected on patients seen during a 2-week mission trip from January 13 to 24, 2020. Data included eye health and the services given.Results: Altogether 5529 patients were seen by the team. Nearly 247 surgeries were performed by ophthalmologists. The most common diagnosis performed was a refractive error, for which 4066 patients (74%) received eyeglasses. Another 835 (15%) had a consultation by an ophthalmologist, of which 260 (5%) received surgery, mostly manual small incision cataract surgery and intraocular lens insertion.Conclusions and Importance: MMI is helping to reduce the prevalence of preventable blindness in the Ciénaga de Oro population; however, further training to increase the capacity of local ophthalmic services is required to improve long-term eye care to the community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".