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Record W2919439817 · doi:10.1136/bmjebm-2018-111070.76

76 Widening disease definitions in gestational diabetes: an evaluation of changing guidelines

2018· article· en· W2919439817 on OpenAlexaff
Christiana Naaktgeboren, Jenny Doust, Paul Glasziou, Julia Lowe, Mariska Leeflang

Bibliographic record

VenueOral Presentations · 2018
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGestational diabetesOverdiagnosisChecklistGuidelineMedicinePopulationFamily medicineDiseaseTerminologyPediatricsPregnancyPsychologyGestationEnvironmental healthPathology

Abstract

fetched live from OpenAlex

<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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.265
GPT teacher head0.455
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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