Bone Grafting the Patellar Defect After Bone–Patellar Tendon–Bone Anterior Cruciate Ligament Reconstruction Decreases Anterior Knee Morbidity: A Systematic Review
Bibliographic record
Abstract
PURPOSE: The aim of this systematic review was to evaluate the impact of bone grafting of patellar defects on reported anterior knee morbidity and subjective outcomes after bone-patellar tendon-bone autograft reconstruction of the anterior cruciate ligament. METHODS: A systematic electronic search of MEDLINE, Embase, Web of Science, and the Cochrane Library was carried out. All English-language prospective randomized clinical trials published from January 1, 2000, to July 24, 2020, were eligible for inclusion. All studies addressing patellar defect grafting were eligible for inclusion regardless of the timing of surgery, graft type, surgical technique, or rehabilitation protocol. RESULTS: A total of 39 studies with 1,955 patients were included for analysis. There were 796 patients in the no patellar grafting (NPG) group, with a mean age range of 22.7 to 33.0 years, and 1,159 patients in the patellar grafting (PG) group, with a mean age range of 17.8 to 34.7 years. The visual analog scale pain score ranged from 1.2 to 5.1 in the NPG group compared with 0.3 to 3.7 in the PG group. The proportion of patients with anterior knee pain ranged from 19% to 81% in the NPG group and from 15% to 32% in the PG group. Moderate to severe kneeling pain was reported in 22% to 57% of patients in the NPG group and 10% of those in the PG group. The percentage of patients with at least 3° of extension loss ranged from 4% to 43% in the NPG group and from 2% to 11% in the PG group. CONCLUSIONS: PG favors decreased anterior knee pain, kneeling pain, and extension loss compared with non-grafted defects; however, the functional outcomes are comparable. Owing to the heterogeneity in reporting, statistical conclusions could not be drawn. LEVEL OF EVIDENCE: Level II, systematic review of Level I and II studies.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".