A Review of 20 Years of Research on Overdiagnosis and Underdiagnosis in the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) Project
Bibliographic record
Abstract
The Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project represents an integration of research methodology into a community-based outpatient practice affiliated with an academic medical centre. The MIDAS project is the largest clinical epidemiological study using semi-structured interviews to assess a wide range of psychiatric disorders in a general clinical outpatient practice. In an early report from the MIDAS project, we found that across diagnostic categories clinicians using unstandardized, unstructured clinical interviews underrecognized diagnostic comorbidity, compared with the results of semi-structured interviews. Moreover, we found that the patients often wanted treatment for symptoms of disorders that were diagnosed as comorbid, rather than principal, conditions. This highlighted the importance, from the patient's perspective, of conducting thorough diagnostic interviews to diagnose disorders that are not related to the patient's chief complaint because patients often desire treatment for these additional diagnoses. While several of the initial papers from the MIDAS project identified problems with the detection of comorbid disorders in clinical practice, regarding the diagnosis of bipolar disorder we observed the emergence of an opposite phenomenon-clinician overdiagnosis. The results from the MIDAS project, along with other studies of diagnosis in routine clinical practice, have brought to the forefront the problem with diagnosis in routine clinical practice. An important question is what do these findings suggest about the community standard of care in making psychiatric diagnoses, and whether and how the standard of care should be changed? The implications are discussed.
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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 | 0.057 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".