The use of muscle-sparing latissimus dorsi myocutaneous flap (MSLD) to minimize functional morbidity in patients with post burn breast deformities
Bibliographic record
Abstract
Background: The Latissimus dorsi (LD) flap plays a crucialrole in breast reconstruction, particularly for post-mastectomypatients or those dealing with post-burn deformities.The pedicled LD emerges as a dependable option for restoringvolume and in inframammary fold reconstruction in post-burnpatients. Nevertheless, a comprehensive assessment of its potentiallong-term functional effects is imperative prior to optingfor the pedicled LD in breast reconstruction.Objective: This study aims to evaluate the functional outcomesand patient satisfaction in breast reconstruction usingthe (LD) flap versus the (MSLD) flap in post-burned patients.Methodology: In this Ambidirectional Cohort Study(Combined retrospective and prospective cohort study), weenrolled twenty-four female patients who already underwentcorrection of post burn breast deformities to address post-burnbreast deformities between 2020 and 2022. The included patientswere divided into two groups; group I: (14 patients) withtraditional latissimus dorsi LD flap, group II (10 patients): UnderwentMSLD flap. Patient satisfaction was assessed usingthe Breast Q questionnaire, we also used the Western OntarioShoulder Instability Index (WOSI) to evaluate shoulder functionscores, and the functional assessment involved both objectivemeasures, such as geometric measurements of shoulderrange of movement and radiological assessment of the acromion-humeral interval (AHI).Results: Concerning the Breast Q and WOSI results, therewas statistically significant greater satisfaction in the MSLDflap group. As regarding the deficit in the range of shouldermovement and the AHI measurements, no statistically significantdifferences were observed between both groups, however,the MSLD flap group exhibited superior results.Conclusion: For post-burn breast reconstruction, MSLDshould be preferred, especially for younger patients. If MSLDis impractical, traditional LD is an acceptable alternative butwith shoulder limitations lasting up to 6 months post-surgery.Preoperative imaging to identify abnormal AHI, thus, preventingshoulder impingement and initiating early postoperativeshoulder exercises would help to achieve better outcomes.
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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 | 0.000 | 0.000 |
| 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".