Intraoperative pivot‐shift accelerometry combined with anesthesia improves the measure of rotatory knee instability in anterior cruciate ligament injury
Bibliographic record
Abstract
PURPOSE: The knee stiffness acquired following an Anterior Cruciate Ligament (ACL) injury might affect clinical knee tests, i.e., the pivot-shift maneuver. In contrast, the motor effects of spinal anesthesia could favor the identification of rotatory knee deficiencies prior to ACL reconstruction. Hence, we hypothesized that the intra-operative pivot-shift maneuver under spinal anesthesia generates more acceleration in the lateral tibial plateau of patients with an injured ACL than without. METHODS: Seventy patients with unilateral and acute ACL rupture (62 men and 8 women, IKDC of 55.1 ± 13.8 pts) were assessed using the pivot-shift maneuver before and after receiving spinal anesthesia. A triaxial accelerometer was attached to the skin between Gerdys' tubercle and the anterior tuberosity to measure the subluxation and reduction phases. Mixed ANOVA and multiple comparisons were performed considering the anesthesia and leg as factors (alpha = 5%). RESULTS: , p < 0.001). There was a presence of significant interaction between leg and anesthesia conditions (p < 0.001). CONCLUSIONS: The pivot-shift maneuver performed under anesthesia identifies better rotatory instability than without anesthesia because testing the pivot-shift without anesthesia underestimates the rotatory subluxation of the knee by an increased knee stiffness. Thus, testing under anesthesia provides a unique opportunity to determine the rotational instability prior to ACL reconstruction.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".