Testing relationships: ethical arguments for screening for type 2 diabetes mellitus with HbA1C
Bibliographic record
Abstract
Since the 1990s, glycated haemoglobin (HbA1C) has been the gold standard for monitoring glycaemic control in people diagnosed as having either type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM). Discussions are underway about diagnosing diabetes mellitus on the basis of HbA1C titres and using HbA1C tests to screen for T2DM. These discussions have focused on the relative benefits for individual patients, with some attention directed towards reduced costs to healthcare systems and benefits to society. We argue that there are strong ethical reasons for adopting HbA1C-based diagnosis and T2DM screening that have not yet been articulated. The rationale includes the differential impact of HbA1C-based diabetic testing on disadvantaged groups, and what we are beginning to learn about HbA1C vis-à-vis population health. Although it is arguable that screening must primarily benefit the individual, using HbA1C to diagnose and screen for T2DM may promote a more just distribution of health resources and lead to advances in investigating, monitoring and tackling the social determinants of health.
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 | 0.103 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.044 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".