Knee bracing and anterolateral rotatory
Bibliographic record
Abstract
We reviewed the efficiency of bracing in 85 patients with anterolateral rotatory instability (ALRI). We were able to compare 18 patients using an antirotational brace with 67 using the AC brace (J. E. Hanger, Mon-treal, Canada). We found the AC brace to offer better overall stability, 71 % compared to 50%. The physician caring for the athlete with a tear of the ACL has to decide whether to treat the patient surgically or nonsurgically. In recent years the pendulum has swung from a time when anterior cruciate tears were not thought to be disabling, ’ to a time when it was thought that surgical reconstruction was the best way to treat functional instabil-ity. ’ Literature is now proving that many patients with an anterior cruciate tear can function quite well without sur-gical repair. 2,5 5 We reviewed 85 patients with ALRI6 who had not under-gone any form of anterior cruciate repair. Our goal was to determine the efficacy of bracing to control the ALRI. It had been our experience and that of others that the current braces in use, particularly the antirotational brace, ’ were not adequately controlling the instability associated with an anterior cruciate tear. For this reason, a new brace was designed (Fig. 1). The AC brace relies on a polycentric joint with an extension stop and a pretibial mold to help decrease abnormal tibial rotation and subluxation as the knee ap-proaches extension. The tibial mold also helps prevent the brace from slipping, relying on principles of patellar tendon bearing prosthetics in below the knee amputees. It also provides a broad area upon which the tibial crest can abut as the extension stop comes into play. There is a posterior strap to help prevent the knee from backing out of the brace as it extends. * Address correspondence and reprint requests to L Coughlm, MDCM,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".