Defining and Evaluating Overdiagnosis in Mental Health: A Meta-Research Review
Bibliographic record
Abstract
BACKGROUND: Overdiagnosis is thought to be common in some mental disorders, but it has not been defined or examined systematically. Assessing overdiagnosis in mental health requires a consistently applied definition that differentiates overdiagnosis from other problems (e.g., misdiagnosis), as well as methods for quantification. OBJECTIVES: Our objectives were to (1) describe how the term "overdiagnosis" has been defined explicitly or implicitly in published articles on mental disorders, including usages consistent (overdefinition, overdetection) and inconsistent (misdiagnosis, false-positive test results, overtreatment, overtesting) with accepted definitions of overdiagnosis; and (2) identify examples of attempts to quantify overdiagnosis. METHOD: We searchedPubMed through January 5, 2019. Articles on mental disorders, excluding neurocognitive disorders, were eligible if they usedthe term "overdiagnosis" in the title, abstract, or text. RESULTS: We identified 164 eligible articles with 193 total explicit or implicit uses of the term "overdiagnosis." Of 9 articles with an explicit definition, only one provided a definition that was partially consistent with accepted definitions. Of all uses, 11.4% were consistent, and 76.7% were related to misdiagnosis and thus inconsistent. No attempts to quantify the proportion of patients who were overdiagnosed based on overdetection or overdefinition were identified. CONCLUSIONS: There are few examples of mental health articles that describe overdiagnosis consistent with accepted definitions and no examples of quantifying overdiagnosis based on these definitions. A definition of overdiagnosis based on diagnostic criteria that include people with transient or mild symptoms not amenable to treatment (overdefinition) could be used to quantify the extent of overdiagnosis in mental disorders.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".