The Ptolemy project: a scalable model for delivering health information in Africa
Bibliographic record
Abstract
How is Africa to build up the medical research it needs?Doctors in African research communities are starved of access to the journals and texts their colleagues in more developed countries regard as fundamental to good practice and research.Isolation, burden of practice, and resource limitations make education and research difficult, but the rapid spread of access to the internet reduces these obstacles and provides an increasingly attractive means to disseminate information and build partnerships in education and research.The role of electronic health information in building local capacity to find, publish, and implement solutions has been emphasised recently in Science, 1 Nature, 2 the Lancet, 3 4 and the BMJ. 5 The Global Forum for Health Research gives priority to interventions designed to build research capacity in developing countries and correct the disparity in health research.6 The Coalition for Global Health Research (Canada) has recently reported how a major effort now can make a substantial difference.7 Access to reliable health information has been described as "the single most cost-effective and achievable strategy for sustainable improvement in health care."8 We are interested in helping to build research, teaching, and clinical capacity for neglected yet substantial problems such as injury, which kills 5.1 million people annually.[9][10][11] Origins of the Ptolemy projectThe Ptolemy project was conceived in discussion between a surgeon and a librarian.The surgeon (MB) had recently returned from working in Africa and Afghanistan and was aware how his colleagues there were starved for medical literature.The librarian (WH) is electronic resources coordinator at the University of Toronto Library, the largest academic library in Canada and the third largest in North America, and was interested in expanding access to full text health information in and from developing countries.Two further partners soon joined the discussion.Bioline International, also housed at our university, provides electronic publication for several scientific journals from developing countries, including the East African Journal of Medicine and the Central African Journal of Medicine.The Association of Surgeons of East Africa represents the 400 surgeons who care for
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".