One-Stage Soft Tissue Reconstruction Following Sarcoma Excision: A Personalized Multidisciplinary Approach Called “Orthoplasty”
Bibliographic record
Abstract
Background and Objectives. Wide surgical resection is a relevant factor for local control in sarcomas. Plastic surgery is mandatory in demanding reconstructions. We analyzed patients treated by a multidisciplinary team to evaluate indications and surgical approaches, complications and therapeutic/functional outcomes. Methods. We analyzed 161 patients (86 males (53%), mean age 56 years) from 2006 to 2017. Patients were treated for their primary tumor (120, 75.5%) or after unplanned excision/recurrence (41, 25.5%). Sites included lower limbs (36.6%), upper limbs (19.2%), head/neck (21.1%), trunk (14.9%) and pelvis (8.1%). Orthoplasty has been considered for flaps (54), skin grafts (42), wide excisions (40) and other procedures (25). Results. At a mean follow-up of 5.3 years (range 2–10.5), patients continuously showed no evidence of disease (NED) in 130 cases (80.7%), were alive with disease (AWD) in 10 cases (6.2%) and were dead with disease (DWD) in 21 cases (13.0%). Overall, 62 patients (38.5%) developed a complication (56 minor (90.3%) and 6 major (9.7%)). Flap loss occurred in 5/48 patients (10.4%). The mean Musculoskeletal Tumor Society (MSTS) and Toronto Extremity Salvage Score (TESS) was 74.8 ± 14 and 79.1 ± 13, respectively. Conclusions. Orthoplasty is a combined approach effective in management of sarcoma patients, maximizing adequate surgical resection, limb salvaging and functional recovery. One-stage reconstructions are technically feasible and are not associated with increased risk of complications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".