Correlation of Hallux Rigidus Grade With Motion, VAS Pain, Intraoperative Cartilage Loss, and Treatment Success for First MTP Joint Arthrodesis and Synthetic Cartilage Implant
Bibliographic record
Abstract
BACKGROUND: Grading systems are used to assess severity of any condition and as an aid in guiding treatment. This study examined the relationship of baseline motion, pain, and observed intraoperative cartilage loss with hallux rigidus grade. METHODS: A prospective, randomized study examining outcomes of arthrodesis compared to synthetic cartilage implant was performed. Patients underwent preoperative clinical examination, radiographic assessment, hallux rigidus grade assignment, and intraoperative assessment of cartilage loss. Visual analog scale (VAS) score for pain was obtained preoperatively and at 24 months. Correlation was made between active peak dorsiflexion, VAS pain, cartilage loss, and hallux rigidus grade. Fisher's exact test was used to assess grade impact on clinical success ( P < .05). RESULTS: In 202 patients, 59 (29%), 110 (55%), and 33 (16%) were classified as Coughlin grades 2, 3, and 4, respectively. There was no correlation between grade and active peak dorsiflexion (-0.069, P = .327) or VAS pain (-0.078, P = .271). Rank correlations between grade and cartilage loss were significant, but correlations were small. When stratified by grade, composite success rates between the 2 treatments were nearly identical. CONCLUSIONS: Irrespective of the grade, positive outcomes were demonstrated for both fusion and synthetic cartilage implant. Clinical symptoms and signs should be used to guide treatment, rather than a grade consisting of radiographic, symptoms, and range of motion factors. LEVEL OF EVIDENCE: Level II, randomized clinical trial.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".