Bibliographic record
Abstract
Purpose: The past several decades have seen considerable interest in identifying and applying threshold change values with outcome measures commonly used by physiotherapists. The crucial question of interest to clinicians is, To what extent can valid inferences be drawn from an outcome measure’s change or improvement score? To date, typical reporting by researchers includes the presentation of a validity coefficient, often in the form of the area under a receiver operating characteristic curve, and a threshold change or improvement value. A limitation of existing work is that it does not convey the confidence that a clinician can have in a decision based on applying the proposed threshold change value. Methods: This knowledge translation article presents three questions, or building blocks, to consider when making a judgment about a patient’s change status: (1) to what extent does a measure assess change in the context of interest, (2) what is the threshold change value, and (3) how confident can a clinician be in making the correct decision about a patient’s change status when applying the threshold change value? Results: This article provides a process for combining clinical expertise with the results from threshold value studies to enhance confidence in clinical decisions about individual patients’ change status. Conclusions: The article shows how a graph can be used to efficiently translate the results from threshold value studies to convey the chance of making the correct decision about a patient’s change status.
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 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 | 0.044 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".