Decision thresholds and minimal important difference estimates for evidence-based practice and policy (Part 2)
Bibliographic record
Abstract
Understanding core concepts in epidemiology and biostatistics is crucial for evidence-based clinical practice and policy. In this second installment of our two-part series on threshold concepts, we transition from understanding the ubiquitous p-value to tools and measures for decision making among clinicians-in-training, highlighting the growing importance of utilizing explicit and evidence-based approaches to make appropriate and efficient decisions. We review two related decision-making concepts: (1) Minimal Important Difference (MID) estimates and (2) Decision Thresholds, focusing specifically on patient-reported outcome measures (PROMs). These terms and many other related expressions are used regularly, and often interchangeably, but what are they? Why are they valuable? And how can they be used to support evidence-based decision-making in clinical contexts and develop strong clinical practice guidelines? We conclude our brief review on the utility of these measures with a spotlight on a local example of how the theory underlying MID estimates and decision thresholds is currently being embedded in electronic platforms in primary care contexts targeting depression in Nova Scotia.
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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 | 0.111 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".