Documentation Committee Subjective Knee Form
Bibliographic record
Abstract
Evaluation and refinement of new techniques are essential for the evolution of every type of surgery. The evaluation of the results achieved is an important link in this chain of improvements. To provide adequate tools for this evalua-tion in knee surgery, several subjective knee scores have been developed during the past years.2,5,7,8,15,16 Most of the available self-administered questionnaires are disease specific instead of knee specific. Questionnaires that are designed as subjective scoring sys-tems can be used in countries other than the ones in which they were developed if they are translated and validated for a specific language and population.10,11,17 For the Netherlands, only the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Oxford 12 Questionnaire score are translated and validated for use in the Dutch popu-lation. Both are especially designed for osteoarthritis and not for use in other knee-related problems. In 2004, the WOMAC was translated and validated for Dutch patients with osteoarthritis by Roorda et al.19 It is widely used and is translated and validated in several lan-guages. This disease-specific questionnaire measures health-related quality of life by mainly scoring pain and physical functioning.2,19 The Oxford 12 Questionnaire score was translated and validated for the Dutch population by Haverkamp et al12 in
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.048 |
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".