When Minimal Detectable Change Exceeds a Diagnostic Test–Based Threshold Change Value for an Outcome Measure: Resolving the Conflict
Bibliographic record
Abstract
Assessing patient progress is an integral part of physical therapist practice. In an attempt to assist clinical decision making regarding a patient's change status, researchers have offered study-based threshold change values. Often researchers have provided reliability and diagnostic test-based estimates of threshold change values obtained from the same patient sample. A potential dilemma occurs when the reliability (ie, the minimal detectable change [MDC])-based threshold change value exceeds the diagnostic test-based threshold value. How can a change be detected if the threshold change value falls within the limits of error? In this situation, researchers have recommended using the larger MDC threshold change value. In this perspective article, we describe the interpretation of the threshold values provided by each of these estimation methods and consider which one offers information that is more meaningful to the challenge faced by physical therapists when making decisions concerning the change status of patients. The context for our discussion is a clinical vignette that depicts the dilemma outlined above. We conclude this perspective with suggestions for researchers concerning essential information to include when reporting threshold estimates obtained from reliability-based and diagnostic test-based studies of outcome measures.
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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 | 0.275 | 0.595 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier 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".