The need for improved patient reported outcome measures in patients with extremity sarcoma: a narrative review
Bibliographic record
Abstract
BACKGROUND: Extremity sarcoma causes impairments to functionality and quality of life. Patient-reported outcome measures (PROMS) assess patient perspectives relating to domains of health and quality of life. METHODS: To describe PROMs utilised in extremity sarcoma, the available literature was screened for studies that utilised PROMs to evaluate outcomes in extremity sarcoma following surgery. RESULTS: Seventy articles met eligibility criteria; six PROMs were identified. The Toronto Extremity Salvage Score, The Short-Form 36, The EORTC QLQ-C30, The Disabilities of the Arm, Shoulder and Hand questionnaire, the Reintegration to Normal Living index and the Patient-Reported Outcomes Measurement Information System. Most sarcoma patients score well in these tools, with bone sarcoma, and extent of resection being predictors of poor outcomes. CONCLUSION: TESS is the only sarcoma-specific PROM, and though a valid assessment of functionality, it has difficulty differentiating patients with minor functional impairments. The absence of a disease-specific measure of health is concerning, as generic tools do not account for the unique experiences sarcoma patients face and may impair their accuracy in analysing intervention effectiveness.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".