Can Orthopedic Oncologists Predict Functional Outcome in Patients with Sarcoma after Limb Salvage Surgery in the Lower Limb? A Nationwide Study
Bibliographic record
Abstract
Accurate predictions of functional outcome after limb salvage surgery (LSS) in the lower limb are important for several reasons, including informing the patient preoperatively and, in some cases, deciding between amputation and LSS. This study aimed to elucidate the correlation between surgeon-predicted and patient-reported functional outcome of LSS in the Netherlands. Twenty-three patients (between six months and ten years after surgery) and five independent orthopedic oncologists completed the Toronto Extremity Salvage Score (TESS) and the RAND-36 physical functioning subscale (RAND-36 PFS). The orthopedic oncologists made their predictions based on case descriptions (including MRI scans) that reflected the preoperative status. The correlation between patient-reported and surgeon-predicted functional outcome was "very poor" to "poor" on both scores (r (2) values ranged from 0.014 to 0.354). Patient-reported functional outcome was generally underestimated, by 8.7% on the TESS and 8.3% on the RAND-36 PFS. The most difficult and least difficult tasks on the RAND-36 PFS were also the most difficult and least difficult to predict, respectively. Most questions had a "poor" intersurgeon agreement. It was difficult to accurately predict the patient-reported functional outcome of LSS. Surgeons' ability to predict functional scores can be improved the most by focusing on accurately predicting more demanding tasks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".