Experience With Store-and-Forward Consultations in Providing Access to Pediatric Endocrine Consultations in Low- and Middle-Income Countries
Bibliographic record
Abstract
Pediatric specialists are often unavailable in low- and middle-income countries. As part of multiple professional associations' efforts to improve access to endocrine expertise globally, a pediatric endocrine teleconsultation network was established on a store-and-forward teleconsultation platform to facilitate focused, language-appropriate advice that can be kept for future reference while bypassing real-time video-conferencing, and obviating the need for a scheduled appointment. User information was recorded, and quality statistics on network performance and qualitative evaluation by referring physicians were analyzed. Over a 3-year period, 81 referrers (88% from Haiti) and 13 pediatric endocrinologists registered onto the network and discussed 47 pediatric endocrine cases, exchanging a total of 412 messages for a median of 7 messages (IQR 5, 11) per case. Diagnoses spanned the spectrum of pediatric endocrine disorders. According to referrers, an appropriate expert was consulted and an answer provided sufficiently quickly in 100% of cases. The answer was well-adapted to their environment in 86%, and referrers were able to follow the advice given in 72%. All but one referrer found the advice helpful, it clarified the diagnosis in 88%, assisted with management in 93%, improved patient's symptoms in 77%, improved function in 77%, and was considered cost-saving in 50%. Perceived benefits of the consultations were academic instruction, setting-adapted advice beyond the scope of guidelines or textbooks, and advancement in the diagnostic process. Pediatric endocrine remote store-and-forward consultations in low- and middle-income countries may provide a reasonable alternative to face-to-face visits, providing clinical and educational benefit, and a potential for cost-saving.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".