Pediatric Diabetes Services in Arab Countries during the First 6 Months of the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Introduction The novel coronavirus disease 2019 (COVID-19) pandemic had an impact on different health services due to the lockdowns, curfews, or reducing provision of some clinical services to infection control. Aims This study looked at the impact of the pandemic on the pediatric diabetes services in Arab countries during the first 6 months of pandemic. Methods and Material An online survey using SurveyMonkey was e-mailed to the pediatric endocrine consultants practicing in Arab countries. The survey was active for the last 2 weeks of August 2020. Results Responses received from 34 consultants. Most responses were from Saudi Arabia, Iraq, and Oman. 18% of consultants have more than 500 pediatric patients with type-1 diabetes mellitus (T1DM) under each of their care. A quarter of responding consultants had suspended their clinics completely during the pandemic period. The median number of children live with diabetes used to be seen in diabetes clinics has dropped significantly from 22 to only 4 patients per week during the pandemic. Half of respondents fed back those virtual clinics were useful. The number of elective and urgent admissions during the studied period were less than before the pandemic. A total of 17.6% of responders reported an increased incidence of Diabetic Ketoacidosis (DKA) during the pandemic. Also, 36.4% thought the research activity had markedly decreased during the COVID-19 pandemic. Conclusion The pandemic has led to reducing the number of patients attending the diabetes clinic in Arab countries. However, virtual clinics and tele-consultations emerged evidently during the pandemic. It is expected to continue using the virtual clinics when the pandemic is over.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".