82 Defining overdiagnosis of mental health disorders: secondary analysis of an overdiagnosis scoping review
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Bibliographic record
Abstract
<h3>Objectives</h3> The term 'overdiagnosis' was first used as early as 1924, when J. D. Adamson used it to describe negative implications of attempts to achieve early diagnosis of pulmonary tuberculosis (CMAJ, 1924). Since then, the term has been used most frequently to describe negative outcomes from screening for early-stage asymptomatic cancers. Typical definitions describe overdiagnosis as detection of early-stage asymptomatic conditions that would never have led to morbidity or mortality. In other areas, including mental health, overdiagnosis can occur among people who experience symptoms but whose symptoms do not reflect disorders and may not be amenable to healthcare interventions. Thus, more recent alternative definitions have focused on diagnosis among people who would not be expected to experience net benefit. The degree to which such definitions have been adopted is not clear. Our objective was to describe how the term overdiagnosis has been defined explicitly or operationalized implicitly in mental health. <h3>Method</h3> A scoping review of overdiagnosis across medical disciplines searched PubMed in August 2017 for published articles that used keywords related to overdiagnosis. Articles from the scoping review were eligible for the present analysis if they were classified in the scoping review as related to mental health, excluding neurocognitive disorders, and if they used the term overdiagnosis in the text of the article and not just in the title. We extracted basic information about the article and whether it included an explicit or implicit definition of overdiagnosis. Explicit definitions were extracted. If the definition was implicit, the reviewer provided an explanation of how overdiagnosis was operationalized in the study or article. Data were extracted by one reviewer with validation by a second reviewer, and any disagreements resolved by consensus. Explicit and implicit definitions were grouped into categories by one investigator and verified by a second investigator. <h3>Results</h3> 148 articles were included. Of the 14 articles that explicitly defined overdiagnosis, 9 defined it as a false positive diagnosis, 2 as misdiagnosis (diagnosing people with one disorder rather than another), 1 as diagnosis of an individual who would not be expected to benefit from treatment, and 2 had vague descriptions. In the other 134 articles, implicit definitions fit into 4 categories; 68 articles implicitly defined overdiagnosis as diagnosis of people who do not meet diagnostic criteria, 59 as misdiagnosis, 13 as diagnosis resulting from overly broad or changed diagnostic criteria; and 2 as no net benefit from diagnosis. There were 13 with unclear or difficult to classify definitions. There was overlap of definitions with several articles fitting into more than one category. The most significant overlap involved 13 articles that were classified as both misdiagnosis and diagnosis of people who do not meet diagnostic criteria. <h3>Conclusions</h3> Definitions of overdiagnosis commonly used in the context of screening for asymptomatic early-stage disease are not generally applicable in mental health where diagnoses are not made in the absence of symptoms. There is not, however, an agreed upon definition of overdiagnosis in mental health. Results from the present review indicate that the term is used most commonly in the field to describe potential drivers of overdiagnosis, including diagnosing individuals who do not meet diagnostic criteria and overly broad diagnostic criteria, as well as misdiagnosis, which may not always reflect overdiagnosis. Some articles define overdiagnosis in mental health as occurring when there is no net benefit from diagnosis; that is, when individuals are diagnosed, but expected benefits from the diagnosis would not be expected to exceed harms. Agreement on an approach to defining overdiagnosis in mental health is needed so that evidence of overdiagnosis can be more readily evaluated.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it