The Society to Improve Diagnosis in Medicine’s legacy: building a foundation for diagnostic excellence
Bibliographic record
Abstract
Abstract The Society to Improve Diagnosis in Medicine (SIDM) played a pivotal role in elevating diagnostic error from an overlooked aspect of patient safety to a recognized healthcare priority during its thirteen-year history (2011–2024). Through strategic advocacy, coalition building, and engagement with policymakers, SIDM secured dedicated federal funding for diagnostic safety research and promoted diagnostic excellence as a critical healthcare imperative. This article examines the organization’s establishment, evolution and lasting impact on the field of diagnostic safety across research, education, practice improvement, and patient engagement. A crowning achievement was SIDM’s success in stimulating the Institute of Medicine to study the problem, resulting in the landmark 2015 report Improving Diagnosis in Health Care (1). Despite the transformative impact of this report, substantial challenges remain in reducing harm from diagnostic error. We conclude with a call to address gaps in three critical areas: awareness, measurement, and implementation.
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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 | 0.088 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.016 | 0.035 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".