Bibliographic record
Abstract
The first article in this series drew attention to the evidence that ligament damage is more common than any other type of knee injury pathology.The mean delay from anterior cruciate ligament (ACL) injury to diagnosis at a specialist knee clinic was reported as 22 months.The management of the resulting instability from isolated ligament injuries and from more complex injuries is the subject of this the next article in the series. The paper has been written by Derek BickerstaV and Trinath Kakarlapudi from the Northern General Hospital in Sheffield.Derek BickerstaV has a special interest in complex knee injuries and revision surgery.He is a member of the advisory committee producing national guidelines for the practice of ACL reconstruction and he has close associations with top level teams in several sports.Since his sports surgery fellowship in Adelaide, Australia, he has published and presented prolifically on the subject of knee injuries.His colleague, Trinath Kakarlapudi, is a specialist registrar who has just begun a fellowship in knee surgery in Toronto, Canada.The article addresses the issues of assessment, treatment, and rehabilitation of the major isolated ligament injuries as well as the common combined instability patterns.It makes the point that treatment is aimed at restoring stability and function rather than preventing long term degenerative change, as there is no evidence that reconstruction reduces the development of arthritis.However, evidence is emerging that ACL reconstruction may reduce the incidence of subsequent meniscal damage and allow retention of their protective eVect.ACL reconstruction is now such a popular and common operation world wide that data comparing the long term results of modern methods of reconstruction with the natural history of the deficient knee will soon be available.For the future, the challenges mentioned are prevention of associated degenerative change and advances in prosthetic structures rather than grafts.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".