Status of diabetes management among children in the Katako-Kombe health zone, Democratic Republic of Congo, 2024
Bibliographic record
Abstract
Background Managing diabetes in children requires a distinct approach from that of adults, involving trained teams to prevent complications and support families. Evidence from rural areas of the Democratic Republic of Congo is scarce. This study describes pediatric diabetes management in the Katako-Kombe Health Zone, Sankuru province. Methods A retrospective case series was conducted from July 1 to 31, 2024, across 13 facilities providing diabetes care. Thirty-two medical records of children aged 0–18 years and 55 healthcare providers were included through exhaustive sampling. Data were collected via documentary review and structured interviews using the ODK application. Records were assessed for completeness, and missing or inconsistent data were noted. Provider interviews were pre-tested, though formal validation was not performed. Data were analyzed in SPSS 25 using descriptive statistics. Findings reflect facility-based cases and cannot be generalized to the wider population. Results The mean age of children was 10.9 ± 4.2 years, with a male-to-female ratio of 1.5. Less than one-quarter were adolescents (15–18 years). School dropout affected one in four. Nearly 60% were unaware of family history, though 60% adhered to medical appointments. Over a quarter were followed at the General Referral Hospital. Limited follow-up and restricted access to insulin and monitoring devices increased risks of complications, including hypoglycemia and neuropathies. Conclusions Diabetes care for children in Katako-Kombe is fragile. Strengthening local capacity, improving access to treatment, and raising community awareness are urgent priorities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".