76 Widening disease definitions in gestational diabetes: an evaluation of changing guidelines
Bibliographic record
Abstract
<h3>Objectives</h3> The incidence of gestational diabetes mellitus (GDM) is rapidly increasing worldwide, raising a concern of overdiagnosis. While population trends such as obesity, increased age at motherhood, and ethnic changes play a role in this increase, another major cause is the widening of the diagnostic criteria. The primary aim of this study is to evaluate what factors were taken into consideration when new diagnostic criteria for GDM were made. The Guidelines International Network (G-I-N) Preventing Overdiagnosis workgroup recently developed guidance for modifying the definition of disease, including a checklist for items to consider when widening disease definitions.<sup>1</sup> The secondary aim of this study was to pilot the use of this G-I-N checklist. <h3>Method</h3> Documents in which currently used criteria for gestational diabetes were proposed were the focus of this study. Changes to thresholds, timing of testing, and the combination of abnormal test results required were considered changes to definitions, but changes to screening strategies were considered outside the scope. These definition documents were found by backward reference searching from 5 recent reviews of gestational diabetes and through searching of websites of professional and guideline organizations. Documents containing new definitions were assessed against the 8-item G-I-N checklist to evaluate what domains were considered when proposing a new disease definition. <h3>Results</h3> We identified 14 documents which proposed modifying the diagnosis of GDM. Four types of definitions were observed: a percentile definition similar to laboratory reference ranges (n=4); harmonization with type two diabetes mellitus (n=6); a risk-based assessment examining the risk of maternal and fetal adverse outcomes (n=3); and one informed by a health technology assessment (n=1). None of the 14 documents considered all 8 criteria in the G-I-N checklist. All described the new definition in detail and all but one described the trigger. None estimated the impact on the prevalence of GDM. The prognostic ability of the definitions was only assessed by risk-based criteria (n=3) and little attention was given to accuracy, repeatability, or reproducibility (n=2). Potential benefits were mentioned by half (n=7) and harms by fewer (n=4). The balance between harms and benefits was only discussed by 3. <h3>Conclusions</h3> Our analysis of the changes to criteria for GDM reveals a complex history of definitions stemming from 4 conceptual bases. There appears to be a paucity of primary research data used in the development of definitions for GDM. While harms and benefits of changing the definition were sometimes mentioned, there was no explicit consideration or quantification of the benefits versus harms, making thresholds chosen appear arbitrary. Given the impact of seemingly modest changes to disease definitions have on the incidence of disease, we consider definitional changes to be a substantial task for guidelines, one that requires a separate panel. Such panels should use G-I-N’s published 8-item checklist of elements to consider when modifying definitions. For key items, rapid systematic reviews should be considered. Finally, panels could be more cautious in applying dichotomous disease labels and instead use a stratified terminology that reflects a spectrum of risk. <h3>REFERENCE</h3> 1. Doust J, Vandvik PO, Qaseem A, et al. Guidance for modifying the definition of diseases a checklist. <i>JAMA Intern Med</i> 2017;<b>177</b>(7):1020–1025. doi:10.1001/jamainternmed.2017.1302
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".