Management of anterior cruciate ligament tears in Tanner stage 1 and 2 children: a narrative review and treatment algorithm guided by ACL tear location
Bibliographic record
Abstract
The incidence of anterior cruciate ligament (ACL) tears in skeletally immature patients has acutely increased over the last 20 years, yet there is no consensus on a single "best treatment." Selection of an optimal treatment is critical and based on individual circumstances; consequently, we propose a treatment-selection algorithm based on skeletal development, ACL tear location, type, and quality, as well as parental perspective in order to facilitate the decision-making process. We combined our surgical group's extensive case histories of ACL tear management in Tanner Stage 1 and 2 patients with those in the literature to form a consolidated data base. For each case the diagnostic phase, communication with patient and parents, treatment choice(s), selected surgical techniques and rehabilitation schedule were critically analyzed and compared for patient outcomes. MRI-imaging and intraoperative tissue quality assessment were preeminent in importance for selection of the optimal treatment strategy. Considerations for selecting an optimal treatment included: associated lesions, the child/patient and parent(s)' well-informed and counseled consent, biological potential, and the potential for successful ACL preservative surgery. Complete ACL tears were evaluated according to tear-location. In type I and II ACL tears with remaining good tissue quality, we propose primary ACL repair. In type III and IV ACL tears we propose physeal-sparing reconstruction with an iliotibial band graft. Finally, in the case of a type V ACL tear, we propose that the best treatment be based on the Meyers-McKeever classification. We present a facile decision-making algorithm for ACL management in pediatric patients based on specific elements of tissue damage and status.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".