use of HbA1c for new diagnosis of diabetes in those with hyperglycaemia on admission to or attendance at hospital urgently requires research
Bibliographic record
Abstract
The prevalence of diabetes in Birmingham is 11% but it is 22% in hospital inpatients. Queen Elizabeth Hospital in Birmingham (QEHB) serves a multi-ethnic population with 6% Afro-Caribbean, 19% South Asian and 70% White European. A clinical audit of 18,965 emergency admissions to QEHB showed that 5% were undiagnosed but had admission glucose in the ‘diabetes’ range and 16% were in the ‘at risk’ range. The proportion of Afro-Caribbeans (7%) and South Asians (8%) in the ‘diabetes’ range was higher than White Europeans (5%). Given the magnitude of the problem, this paper explores the issues concerning the use of reflex HbA1c testing in the UK for diagnosis of diabetes in hospital admissions. HbA1c testing is suitable for most patients but conditions affecting red blood cell turnover invalidate the results in a small number of people. However, there are pertinent questions relating to the introduction of such testing in the NHS on a routine basis. Literature searches on a topical question ‘Is hyperglycaemia identified during emergency admission/attendance acted upon?’, were performed from 2016 to 2021 and 2016 to 2022. They identified 21 different, relevant, research papers - 5 from Australia, 9 from Europe including 4 from the UK, 5 from America and 1 each from Canada and Africa. These papers revealed an absence of established procedures for the management and follow-up of routinely detected hyperglycaemia using HbA1c when no previous diabetes diagnosis was recorded. Further work is required to determine the role of reflex HbA1c testing for diagnosis of diabetes in admissions with hyperglycaemia, and the cost-effectiveness and role of point-of-care HbA1c testing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".