Bibliographic record
Abstract
![Graphic][1] Between August 2014 and July 2015, Dr. Meier taught as a visiting professor at Mbarara University of Science and Technology (MUST) in Uganda, where he mentored pathology resident-equivalents and worked on capacity building in the pathology department. ASCP facilitated support for his time there . MUST, which began 26 years ago as the second medical faculty in Uganda, hosts the Massachusetts General Hospital (MGH) Global Health Collaborative, through which Seed Global Health volunteers come to train and work with Ugandan medical and nursing personnel. As a temporary employee of the pathology department of MGH, I came to Mbarara to contribute to Seed's effort for the academic year 2014-2015. Dr. Meier and Professor Damaris Laffita review a case with MUST pathology MMeds, resident equivalents. My colleagues at MUST included teachers and practitioners from Cuba, Great Britain, Germany, Canada, Australia, New Zealand, and organizations such as Seed and the Peace Corps Response, the latter part of the Global Health Service Partnership. Damaris Laffita, professor of pathology at MUST and an excellent, patient, and kind colleague, is the only full-time onsite pathologist at the university. She combines a knowledge base and technical vocabulary familiar to North American pathologists with experience in making defensible diagnoses in resource-constrained settings, an advantage for practice in Africa. Each semester I taught 20 large-group lectures, all with associated practical sessions. Seventy second-year medical students, as well as another 40 nursing and allied health students, attended lectures on general pathology, and similar numbers of third-year students attended lectures on systemic pathology topics. During the spring 2015 semester, Professor Damaris and I also conducted daylong seminars and tutorials on pathologic topics relevant to residents in surgical specialties (general surgery, obstetrics/gynecology, and ophthalmology) as well as for master's-degree candidates in medical technology. While teaching pathology to medical and other allied health … [1]: /embed/inline-graphic-1.gif
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".