Practicing medicine without borders: tele‐consultations and tele‐mentoring for improving paediatric care in a conflict setting in Somalia?
Bibliographic record
Abstract
OBJECTIVES: In a district hospital in conflict-torn Somalia, we assessed (i) the impact of introducing telemedicine on the quality of paediatric care, and (ii) the added value as perceived by local clinicians. METHODS: A 'real-time' audio-visual exchange of information on paediatric cases (Audiosoft Technologies, Quebec, Canada) took place between clinicians in Somalia and a paediatrician in Nairobi. The study involved a retrospective analysis of programme data, and a perception study among the local clinicians. RESULTS: Of 3920 paediatric admissions, 346 (9%) were referred for telemedicine. In 222 (64%) children, a significant change was made to initial case management, while in 88 (25%), a life-threatening condition was detected that had been initially missed. There was a progressive improvement in the capacity of clinicians to manage complicated cases as demonstrated by a significant linear decrease in changes to initial case management for meningitis and convulsions (92-29%, P = 0.001), lower respiratory tract infection (75-45%, P = 0.02) and complicated malnutrition (86-40%, P = 0.002). Adverse outcomes (deaths and lost to follow-up) fell from 7.6% in 2010 (without telemedicine) to 5.4% in 2011 with telemedicine (30% reduction, odds ratio 0.70, 95% CI: 0.57-0.88, P = -0.001). The number needed to be treated through telemedicine to prevent one adverse outcome was 45. All seven clinicians involved with telemedicine rated it to be of high added value. CONCLUSION: The introduction of telemedicine significantly improved quality of paediatric care in a remote conflict setting and was of high added value to distant clinicians.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".