Reconstruction of Upper Limb Long Bone Defects with Vascularized Free Fibula in Pediatric Population: A Review of the Functional Outcomes
Bibliographic record
Abstract
Objective: The aim of our study was to evaluate the functional outcomes in paediatric patients who underwent upper limb long bone reconstruction with vascularised free fibula. Study Design: Retrospective descriptive study. Place and Duration of study: Department of Plastic Surgery, Shifa international Hospital, Islamabad Pakistan from Jun 2015 to Jul 2020. Methodology: All paediatric patients who underwent upper limb long bone defect reconstruction secondary to sarcoma resection were included in the study. Radiological and functional assessment was done pre and post operatively. Outcomes were assessed in terms of surgical wound dehiscence, flap loss, donor site morbidities and functional outcomes. Functional outcomes were evaluated using Musculoskeletal Tumor Scores for upper limb and pediatric Toronto Extremity Salvage Score for upper limb. Results: Total of 14 paediatric patients were operated during 5-year period of June 2015-July 2020. Age of the patients ranges from 9 - 15(12.5) years. All the limbs were successfully salvaged with no flap loss and no significant donor site morbidity. Functional latissimus dorsi flap was done in 6 patients to achieve elbow flexion. Overall musculoskeletal tumor society Score was 82% and pediatric toronto exteremity salvage score mean was 82.43%. Conclusion: Vascularised free fibula is a reliable and cost-effective option for bridging bone gaps in paediatric patients after sarcoma resection with optimal functional 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".