Bibliographic record
Abstract
Background Injury results in a limitation of activity participation. Treatment of injury is designed to return the patient to as a high level of activity as desired. Objective To design an activity measure that allows clinicians to: Determine patients' pre-injury preferred activities, level of participation, intensity, and frequency of participation. Determine patients' recovery level of participation, intensity, and frequency of participation in the pre-injury preferred activities. Determine if the patients' lack of full return to activity is due to the injury or to other reasons external to the injury. Setting Orthopaedic community based clinics. Participants Patients with musculoskeletal complaints. Intervention Treatment of injury. Main outcome measure The composite of the patients' current activity participation, level of limitation, and reason for limitation. Results Favourite/Most Important Activity Q1 What is this activity? Prior to Your Injury Q2 Prior to your injury: A How long would you normally participate in this activity? |__|__| Hours per Time B On average how many days a week would you participate in this activity? |__| Days per Week Current Time Q3 Do you still engage in this sport or activity? 1 □ Yes 2 □ No Q4 How much is your ability to participate in this activity limited by your injury? 0 1 2 3 4 5 6 7 8 9 10 None Completely If None (0) (Go to Q6 – top of next column) If Limited (1–10) continue with Q5 Q5 What is the primary reason you are limited in this sport or activity anymore? 1 □ Your Injury 2 □ Other Reason –Describe: Conclusion This measure allows clinicians to track return to activity over time and understand if the decision to return to pre-injury activity is due to the injury or life events.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".