Quanto de perda volumétrica e de força do músculo quadríceps femoral éesperada no pós-operatório da reconstrução do ligamento cruzado anterior?
Bibliographic record
Abstract
Introduction: When injured and/or reconstructed the anterior cruciate ligament, not only occurs decrease in strength, but also less contraction of the quadriceps, in addition to muscle atrophy. Magnetic resonance imaging and isokinetic dynamometry have offered better evaluation of the pre- and post-surgical periods and can better monitor and predict postoperative rehabilitation. Objectives: To review the role of volume and strength of the quadriceps femoris muscle before and after reconstruction of the anterior cruciate ligament and how these measurements correlate with the predictive variables of pre- and postoperative muscle strength. Method: Integrative review collecting information on virtual platforms. The texts were selected from SciELO, Google Scholar, Pubmed and Scopus. The descriptors related to the topic were the following: anterior cruciate ligament reconstruction; magnetic resonance imaging; quadriceps muscle in Portuguese and English with AND or OR search, considering the title and/or abstract. Results: The entire selected texts were read and 61 articles were included. Conclusion: A loss of volume and strength of the quadriceps muscle was observed after reconstruction. The loss of strength was 4 times greater than the preoperative volume and 2 times greater postoperatively, with improvement 4 months after the operation.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".