Noise is an underrecognized problem in medical decision making and is known by other names: a scoping review
Bibliographic record
Abstract
Unwanted random variability in day-to-day decision making referred to as 'noise' is associated with unhelpful variation that affects both the reproducibility and quality of decision making. Although this is described in other fields, the prevalence of noise in medical decision making and its effects on patient outcomes and the process and efficiency of care have not been reported and are unknown. This review sought to explore noise as a feature of medical decision making, as well as explore potential sources of noise in this setting. The search generated 2,082 results. Analysis of 14 studies included in the review (11 PubMed, 3 reference mining) suggests noise is a driver of unhelpful practice variation and may have important effects on care efficiency and reproducibility. 7 of the 14 studies demonstrated pattern noise, 3 demonstrated occasion noise, and 5 demonstrated stable pattern noise. The decision making in 8 studies demonstrated level noise, and lastly the decision making in 4 of the studies demonstrated system noise, a combination of both pattern and level noise. Additional study is required to ascertain how to measure and mitigate noise in medical decision making, as well as better understand the sources of noise present. Clinical trial number not applicable.
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 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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".