Return to activity is a multi-faceted variable, not a discrete yes-no
Bibliographic record
Abstract
OBJECTIVES: To develop a patient-reported outcome that allows for tracking of return to activity after injury. By acknowledging that return to activity is not a discrete Yes/No question where participants return to their baseline activity may be unrelated to their treatment a more comprehensive understanding and measurement of the outcome of treatment after injury as it relates to activity participation was developed and evaluated. METHODS: Item development and evaluation were undertaken with the final version tested in an ongoing observational clinical trial. Descriptive statistics and test-re-test analysis using intra-class correlation and percent agreement were used. RESULTS: A 5-item set of questions was identified that assess return to activity from a multi-faceted perspective. The final 5 items record preferred activity, days and hours per week of participation, impact of change in participation in activity, degree of limitation in participation and if it is related to injury or external factors. Over 30% of the population reported that their participation in their preferred activity was no longer active and not related to their injury but other factors demonstrating the importance of documenting more than one variable. CONCLUSION: The Minnesota Activity Scale provides standardized questions to comprehensively assess return to activity as a marker of treatment effectiveness. LEVEL OF EVIDENCE: V.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".