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Record W4414218279 · doi:10.1136/ebm-2025-pod.22

022 From early detection to overdiagnosis: a qualitative exploration of lived experiences with prediabetes and mild diabetes in Canada

2025· article· en· W4414218279 on OpenAlexaffabout
Raha Eskandari, Wade Thompson, Dana Stanley, Jessica Otte, Colleen Fuller, N. Zoe Hilton, Sonia Butalia, Fariba Aghajafari, Anshula Ambasta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of CalgaryDiabetes CanadaiCo Therapeutics (Canada)University of British Columbia
Fundersnot available
KeywordsPrediabetesThematic analysisLived experienceGratitudeReflexivityQualitative researchIntervention (counseling)Disease

Abstract

fetched live from OpenAlex

Objectives The terms ‘prediabetes’ (6.0% ≤ A1c ≤ 6.4%) and ‘mild diabetes’ (6.5% ≤ A1c ≤ 7.0%) describe conditions where blood sugar levels are elevated but still fall within the treatment targets set by national guidelines and specialty societies for type 2 diabetes. While some view these diagnostic labels as offering opportunities for early intervention and delaying disease progression, there are concerns regarding overdiagnosis, with potential consequences including over-testing and overtreatment. Currently, there is limited understanding of patient experiences and priorities regarding the diagnosis and management of pre- or mild diabetes. Method We conducted a qualitative study engaging individuals recently diagnosed with pre- or mild diabetes, in collaboration with health services researchers, healthcare professionals, and patient research partners. Following the Patient Engagement Framework from the Strategy for Patient Oriented Research (SPOR), we worked with three patient research partners from British Columbia, Alberta, and Ontario, Canada. Together, we obtained funding, designed and conducted pre-interview surveys and interviews, and analyzed data to better understand patient experiences and priorities. We used reflexive thematic analysis, following Braun and Clarke’s approach, to develop meaning-based themes. Results Twelve adults have participated in this study so far, with four to eight more anticipated. Of all participants 66.7% had prediabetes and 33.3% had mild diabetes. The majority (58.3%) of participants were in the 40–60 years age range. We developed three main themes: i) While experiencing a range of negative emotions following initial diagnosis, participants often expressed gratitude for early detection and symptom awareness, ii) valued receiving information and education early on, and iii) desired individualized treatment approaches that aligned with their priorities, taking health equity factors into account. As an important subtheme under the third theme, while lifestyle modification was often preferred as the initial approach, many participants also acknowledged pharmacotherapy as a viable option when lifestyle changes had been maximized or could no longer be further optimized. Conclusions This study focuses on the balance between the perceived benefits of early intervention and the risks of overdiagnosis and its consequences. Understanding patient experiences and perspectives in early-stage diabetes highlights the need for shared decision-making and individualized care. Insights will guide the development of tools and resources to help both patients and clinicians shape and navigate informed, patient-centered care. Future work is needed to clarify the evidence for early diagnosis or treatment of people with pre- or mild diabetes to avoid overdiagnosis and overtreatment.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0280.021
Scholarly communication0.0080.004
Open science0.0040.010
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.286
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2025
Admission routes2
Has abstractyes

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