The impact of loading and physical activity measures on outcomes in patients with knee osteoarthritis, and implant survivorship in patients following knee arthroplasty
Bibliographic record
Abstract
Chapter 3: The relationship between knee loading during gait and cartilage thickness in non-traumatic and post-traumatic knee osteoarthritis……………. 41 3.1 Abstract………………………………………………………………………….42 3.2 Introduction…………………………………………………………………...... 43 3.3 Methods………………………………………………………………………… 45 3.4 Results………………………………………………………………………….. 50 3.5 Discussion……………………………………………………………………….59 3.6 Chapter 3 References………………………………………………………….... 64 Preface to Chapter 4……………………………………………………………………….. 67 iii Chapter 4: Vastus medialis intramuscular fat is associated with reduced quadriceps strength, but not osteoarthritis severity……………………………….. 4.1 Abstract………………………………………………………………………….4.2 Introduction…………………………………………………………………….. 70 4.3 Methods………………………………………………………………………… 72 4.4 Results………………………………………………………………………….. 77 4.5 Discussion……………………………………………………………………….4.6 Conclusions…………………………………………………………………….. 88 4.7 Chapter 4 References…………………………………………………………… Preface to Chapter 5……………………………………………………………………….. Chapter 5: Understanding the impact of physical activity level and sports participation on implant integrity and failure in patients following unicompartmental and total knee arthroplasty: a scoping review…………….. 5.1 Abstract………………………………………………………………………….5.2 Background……………………………………………………………………... 5.3 Methods………………………………………………………………………… 99 5.4 Results………………………………………………………………………… 5.5 Discussion…………………………………………………………………….. 113 5.6 Conclusions…………………………………………………………………… 5.7 Chapter 5 References………………………………………………………….. Preface to Chapter 6……………………………………………………………………... Chapter 6: Comparing evoked pain responses and the prospective prognostic value of different measures of sensitivity to physical activity among people with knee osteoarthritis………………………………………………………………… 124 6.1 Abstract……………………………………………………………………….. 125 6.2 Introduction…………………………………………………………………… 6.3 Methods……………………………………………………………………….. 6.4 Results………………………………………………………………………… 6.5 Discussion…………………………………………………………………….. 146 6.6 Conclusions…………………………………………………………………… 6.7 Chapter 6 References………………………………………………………….. Chapter 7: General discussion………………………………………………………... 154 7.1 General discussion…………………………………………………………….. 7.2 Chapter 7 References………………………………………………………….. Chapter 8: Conclusion and summary………………………………………………..
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".