Severe hypoglycaemia in paediatric oncology: characterisation and risk factors
Bibliographic record
Abstract
OBJECTIVES: Primary objective was to describe the cumulative incidence of severe hypoglycaemia in paediatric patients with cancer. Secondary objectives were to determine risk factors for severe hypoglycaemia and to describe its clinical course and management. METHODS: In this single institution retrospective study, for the cumulative incidence cohort, we included cancer diagnosis and hypoglycaemia episodes between June 2018 and November 2021. For the chart review cohort, we included cancer diagnosis January 2009-November 2021 and hypoglycaemia episodes June 2018-November 2021. RESULTS: There were 1237 cancer diagnoses and 142 patients with severe hypoglycaemia in the cumulative incidence cohort. Cumulative incidence at 6 months after cancer diagnosis was 9.4% (95% CI 7.7% to 11.0%). Severe hypoglycaemia incidence significantly increased over time (r=0.77, p=0.004). Independent risk factors were age at diagnosis (HR 0.88, 95% CI 0.85 to 0.91); acute lymphoblastic leukaemia (HR 3.06, 95% CI 2.19 to 4.29) and relapse (HR 9.54, 95% CI 3.83 to 23.76). There were 4672 cancer diagnoses and 267 episodes of severe hypoglycaemia in the chart review cohort. CONCLUSIONS: The cumulative incidence of severe hypoglycaemia 6 months after cancer diagnosis was 9.4%. Severe hypoglycaemia increased over time. Younger patients, those with acute lymphoblastic leukaemia and those with a history of disease relapse, were at higher risk of severe hypoglycaemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".