Bibliographic record
Abstract
Countries in Africa are among the poorest in the world. As a result, they often do not have resources for basic health care. The most common medical problems are related to a variety of infectious diseases, and African countries have the highest incidence rates in the world of life-threatening infections such as tuberculosis, meningitis, malaria, and acquired immunodeficiency syndrome (AIDS). In November 2000 I went to Zimbabwe as a medical volunteer with a Canadian–Jewish humanitarian organization called Veahavta. I worked at a rural Salvation Army hospital, the Howard Hospital, in a small farming community called Gweshe, about 90 km north of Harare, the capital city. The area is primarily agricultural, with numerous small subsistence farms. The hospital has 150 inpatient beds, a very busy outpatient facility, and a regional obstetrical service with more than 3000 deliveries per year. There is one full-time physician, an obstetrician born and trained in Canada. I spent nearly 3 weeks at the Howard Hospital, where I was responsible for medical care for both inpatients and outpatients, assisted at surgical procedures, supervised a clinical research study, and provided educational sessions for nurses and nursing students. As might have been expected, the major medical problems encountered were a variety of infectious diseases, including AIDS, tuberculosis, pneumonia, gastroenteritis, and schistosomiasis. We also treated patients with rheumatic fever, malaria, hepatitis, meningitis, sexually transmitted diseases, pelvic inflammatory disease, mucocutaneous candidiasis, burns, and traumatic wound infections. The only laboratory tests available were hemoglobin level, white blood cell count, blood glucose level, pregnancy testing, Gram staining, acid-fast staining, malaria prep, direct smears for ova and parasites, and VDRL (Venereal Disease Research Laboratory). Microbial cultures were not available. It was possible to perform plain radiography and abdominal ultrasonography, but no other imaging studies. As a result, most of our diagnoses and treatment were empiric. A restricted group of anti-infective agents were available — penicillin, cloxacillin, ampicillin, erythromycin, tetracycline, clindamycin, cotrimoxazole, nalidixic acid, metronidazole, kanamycin, chloroquine, continued on page 7 In November 2000, Andrew Simor, the Head of Microbiology at Sunnybrook and Women’s College Health Sciences Centre, travelled to Zimbabwe as a medical volunteer. He took this issue’s cover picture during his stay. It shows at least 14 people who were undergoing active investigation for tuberculosis (TB) on one day in the TB clinic at Howard Hospital. Out of Africa — Experiences of a Canadian Doctor in Zimbabwe
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".