The frequency of detection of unexpected diabetes mellitus during haemoglobinopathy investigations
Bibliographic record
Abstract
AIMS: To establish the frequency of detection of previously undiagnosed diabetes mellitus as a result of detection of an increased glycated fraction of haemoglobin during high performance liquid chromatography (HPLC) for haemoglobinopathy diagnosis. METHODS: A prospective study was carried out over a 3-month period. During that period a total of 2094 patient samples were received for haemoglobinopathy investigation and were included in the study. RESULTS: Fifty samples were found to have an apparent increase in the glycated haemoglobin fraction and of these 38 were found to be from patients with known diabetes. Previously undiagnosed diabetes was discovered in 11 patients and it is likely that the twelfth patient also had diabetes. CONCLUSIONS: The detection of evidence of undiagnosed diabetes during HPLC haemoglobinopathy investigations is not rare, there being four cases per month in this study. This incidental observation should be reported to clinical staff.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".