The Development of a Multilingual Tool for Facilitating the Primary-Specialty Care Interface in Low Resource Settings: the MSF Tele-Expertise System
Bibliographic record
Abstract
In 2009, Médecins Sans Frontières (MSF) started a pilot trial of store-and-forward telemedicine to support field workers. One network was operated in French and one in English; a third, Spanish network was brought into operation in 2012. The three telemedicine pilots were then combined to form a single multilingual tele-expertise system, tailored to support MSF field staff. We conducted a retrospective analysis of all telemedicine cases referred from April 2010 to March 2014. We also carried out a survey of all users in December 2013. A total of 1039 referrals were received from 41 countries, of which 89% were in English, 10% in French, and 1% in Spanish. The cases covered a very wide range of medical and surgical specialties. The median delay in providing the first specialist response to the referrer was 5.3 h (interquartile range 1.8, 16.4). The survey was sent to 294 referrers and 254 specialists. Of these, 224 were considered as active users (41%). Out of the 548 users, 163 (30%) answered the survey. The majority of referrers (79%) reported that the advice received via the system improved their management of the patient. The main concerns raised by referrers and specialists were the lack of support or promotion of system at headquarters' level and the lack of feedback about patient follow-up. Because of the size of the MSF organization, it is clear that there is potential for further organizational adoption.
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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 | 0.007 | 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 | 0.001 | 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".