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Record W2994701227 · doi:10.1136/bmjebm-2019-pod.40

26 Defining and measuring overdiagnosis when there is no diagnosis of disease: the experience of the canadian task force on preventive health care with overdiagnosis in the context of fracture risk assessment

2019· article· en· W2994701227 on OpenAlexaffabout
Guylène Thériault, Brett D. Thombs, Heather Limburg, Jennifer Pillay, John Brodersen

Bibliographic record

VenueOral Presentations · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of AlbertaPublic Health Agency of CanadaMcGill University
Fundersnot available
KeywordsOverdiagnosisHarmMedicineContext (archaeology)DiseaseGuidelineRandomized controlled trialHealth careIntensive care medicinePhysical therapyPsychologySurgeryPathologySocial psychology

Abstract

fetched live from OpenAlex

‘Overdiagnosis means making people patients unnecessarily, by identifying problems that were never going to cause harm or by medicalising ordinary life experiences through expanded definitions of diseases’ (Broderson et al). That definition seems intuitive and for certain diseases (like cancers), measurement of the extent of this phenomenon has been described. Measurement relies sometimes on data from randomized trials, e.g. the comparison of the number of incident cases in a screened group versus a non-screened group. In high-quality randomized screening trials with sufficient follow-up time and little or no contamination of the control group, the excess number of diagnosed cases in the screened group represents the degree of overdiagnosis. But, what about when there is no disease, when we are identifying problems defined by an estimated risk of a future event? That was the question faced by members of the Canadian Task Force on Preventive Health Care when developing a protocol for an evidence synthesis to support a guideline on screening to prevent fragility fractures. In that setting, the screened group is given a risk of a future event, not a diagnosis that could become apparent in the non-screened group. In fact, there are no symptoms to diagnose before somebody experiences a fracture, so these individuals would experience the outcome, not the ‘disease’ hereby defined as a risk. For the Task Force, in the setting of screening to prevent fragility fractures, overdiagnosed individuals are those who are deemed to be at excess risk of fracture – either according to a set threshold or based on shared decision-making –but who would have never known they were at risk because, without screening, they would not have experienced a fracture. We will explain the process that lead us to this definition and will give our perspective on how to calculate the degree of overdiagnosis when it is not possible to compare the occurrence of disease in screened and not screened groups. We believe this extension of the definition and the proposed way of calculating overdiagnosis in the setting of risk assessment is a way forward in the conceptualization of the overdiagnosis phenomenon. We will suggest that this could be applied to other chronic diseases, including, for example hypercholesterolemia and the risk of cardiovascular disease, where the value of cholesterol is also used (with other factors) to estimate risk and inform decisions. To our knowledge, it is the first time that overdiagnosis in risk assessment has been defined in this manner. Objectives Propose a way to conceptualize overdiagnosis and calculate its extent in the context of risk assessment. Method This is the result of a group reflection on the topic that started while trying to define outcomes for a systematic review on the prevention of fragility fractures. Results Starting from more common ways of understanding and calculating overdiagnosis we will present how we propose to achieve this in the setting of risk assessment. We will share the logic we followed and some graphical representation of our idea. Conclusions Overdiagnosis is not an easy concept to grasp when there is no disease. At times this has been simplified by labeling a risk as a disease (ex: osteoporosis is not a disease in itself; it confers a certain amount of risk of fractures). We will share our thoughts about a way to further understanding of overdiagnosis in the context of risk assessment.

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)0.481
metaresearch head score (Gemma)0.522
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4810.522
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0110.015
Science and technology studies0.0170.023
Scholarly communication0.0120.007
Open science0.0110.013
Research integrity0.0100.018
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.185
GPT teacher head0.475
Teacher spread0.291 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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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Citations1
Published2019
Admission routes2
Has abstractyes

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