Insulin pump therapy, pre‐pump hemoglobin A <sub>1c</sub> and metabolic improvement in children with type 1 diabetes at a tertiary Canadian children's hospital
Bibliographic record
Abstract
Background and Objectives Indications for insulin pump therapy (IPT) in children with type 1 diabetes (T1D) are relatively non-specific and therefore subject to provider discretion. Health professionals' perceptions of which people will have difficulty with IPT, for example, those with higher hemoglobin A1c (HbA1c), may not be correct. This study examined the effect of IPT on HbA1c, and the role of pre-pump HbA1c on this effect. Methods All children with T1D started on IPT at British Columbia Children's Hospital from January 2011 through June 2016 were included if they had HbA1c values available both before and after IPT (n = 125). Generalized estimating equations was used to estimate the effects of IPT on HbA1c, stratified by pre-pump HbA1c levels (good: <7.5% [<58 mmol/mol], moderate: 7.5%-9.0% [58-75 mmol/mol], poor: >9.0% [>75 mmol/mol]). Results After adjusting for potential confounders, mean HbA1c decreased by 0.48% [5.2 mmol/mol] (95% confidence interval: −0.64, −0.33% [−7.0, −3.6 mmol/mol]; P < 0.0001) after IPT initiation. The adjusted mean HbA1c decreased by 0.14% [1.5 mmol/mol] (−0.35, 0.07% [−3.8, 0.8 mmol/mol]; P = 0.188), 0.54% [5.9 mmol/mol] (−0.74, −0.34% [−8.1, −3.7 mmol/mol]; P < 0.0001), and 1.08% [11.8 mmol/mol] (−1.69, −0.46% [−18.5, −5.0 mmol/mol]; P = 0.0006) after pump initiation in the good, moderate, and poor pre-pump metabolic control groups, respectively. Conclusions Pre-pump HbA1c appears to play a significant role in the effects of IPT on HbA1c, with the largest decrease in HbA1c seen in the poor pre-pump HbA1c group. Eligibility and consideration for IPT should be expanded to routinely include these children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".