Audit of Microalbumin Excretion in Children with Type I Diabetes
Bibliographic record
Abstract
OBJECTIVE: To investigate prevalence, persistence and clinical correlates of increased microalbumin excretion in random urine samples collected in a paediatric diabetes clinic. METHOD: Random urine samples were collected annually in patients >10 years attending the diabetes clinic in the Royal Hospital for Sick Children, Edinburgh. Albumin excretion is expressed as albumin:creatinine ratio (ACR) and classified as normal (10 mg/mmol), or macroalbuminuria (>47 mg/mmol in females, >35 mg/mmol in males). We analyzed retrospectively results on 421 urine samples collected from 217 patients (109 males), of a median age of 12.3 years (94% 10-16 years) over 3 years. For each sample, the corresponding mean HbA1c over the previous year was calculated. RESULTS: Prevalence of micro- and macro-albuminuria in individual samples was 1% and 0.5% respectively. ACR was equivocal in 10.1% and 4.7% in samples from females and males respectively (p=0.03). HbA1c showed borderline significant differences across ACR groups (p=0.06). Equivocal ACR excretion was associated with slightly higher mean HbA1c (9.5±1.3%) compared to normal albuminuria (9.0±1.1%, p3.5 mg/mmol. The 14-16 years age group patients were most likely to have ACR >3.5 mg/mmol (p=0.05). CONCLUSIONS: Female sex and increasing age, but not HbA1c, were independently associated with increased ACR. A robust mechanism for collection of repeat early morning urine samples from patients with increased ACR in random urine samples, and follow-up of those patients who have persistently high microalbumin excretion are important. It is also important to confirm the usefulness of ACR measurements in random urine samples as a marker of incipent nephropathy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".