Estimating the Threshold Value for Change for the Six Dimensions of the Impairment Inventory of the Chedoke-McMaster Stroke Assessment
Bibliographic record
Abstract
Purpose: Our purpose was to estimate a threshold value for change for the six dimensions of the Impairment Inventory of the Chedoke-McMaster Stroke Assessment and the confidence in labelling a person as having improved or not. Method: Secondary analysis of two data sets, previously reported by two research teams, consisted of two statistical analyses. The first analysis used a multiple of the standard error of measurement to calculate the threshold value for change for the six dimensions. The second analysis used the diagnostic test method to calculate a threshold improvement value and the confidence a clinician had in labelling a person as having improved or not on the leg, foot, and postural control dimensions. Results: The threshold value for change was determined to be 1 impairment point (i.e., stage) for the arm, hand, leg, foot, and postural control dimensions and 2 impairment points for the shoulder pain dimension. The positive predictive values associated with the leg, foot, and postural control dimensions were 74%, 59%, and 65%, respectively. Conclusions: Clinicians can use a change of 1 impairment point for the arm, hand, leg, foot, and postural control dimensions and a change of 2 impairment points for the shoulder pain dimension to identify true change in a patient’s motor recovery.
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".