The Management of Diabetes in Conflict Settings: Focus on the Syrian Crisis
Bibliographic record
Abstract
Humanitarian crises represent a major global health challenge as record numbers of people are being displaced worldwide. The Syrian crisis has resulted in >4 million refugees and 6 million people who are internally displaced within Syria. In 2017, there were 705,700 reported cases of adult diabetes in Syria. During periods of conflict, people with diabetes face numerous challenges, including food insecurity, inadequate access to medications and testing supplies, and a shortage of providers with expertise in diabetes care. Access to insulin represents a major challenge during a crisis, especially for individuals with type 1 diabetes, for whom the interruption of insulin constitutes a medical emergency. In the short term (days to weeks) during a crisis, it is vital to 1) prioritize insulin for patients with type 1 diabetes, 2) ensure continuous access to essential diabetes medications, and 3) provide appropriate diabetes education for patients, with a focus on hypoglycemia and sick-day guidelines. In the long term (weeks to months) during a crisis, it is important to 1) provide access to quality diabetes care and medications, 2) train local and international health care providers on diabetes care, and 3) develop clinical guidelines for diabetes management during humanitarian crises. It is imperative that we work across all sectors to promote the health of people with diabetes during humanitarian response.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".