Twenty-five years of experience with patient-reported outcome measures in soft-tissue sarcoma patients: a systematic review
Bibliographic record
Abstract
PURPOSE: As the importance of the patient's perspective on treatment outcome is becoming increasingly clear, the availability of patient-reported outcome measures (PROMs) has grown accordingly. There remains insufficient information regarding the quality of PROMs in patients with soft-tissue sarcomas (STSs). The objectives of this systematic review were (1) to identify all PROMs used in STS patients and (2) to critically appraise the methodological quality of these PROMs. METHODS: Literature searches were performed in MEDLINE and Embase on April 22, 2024. PROMs were identified by including all studies that evaluate (an aspect of) health-related quality of life in STS patients by using a PROM. Second, studies that assessed measurement properties of the PROMs utilized in STS patients were included. Quality of PROMs was evaluated by performing a COSMIN analysis. RESULTS: In 59 studies, 39 PROMs were identified, with the Toronto Extremity Salvage Score (TESS) being the most frequently utilized. Three studies evaluated methodological quality of PROMs in the STS population. Measurement properties of the TESS, Quick Disability of the Arm, Shoulder and Hand (QuickDASH) and European Organization for Research and Treatment for Cancer Quality of Life Questionnaire (EORTC-QLQ-C30) were reported. None of the PROMs utilized in the STS population can be recommended for use based on the current evidence and COSMIN analysis. CONCLUSION: To ensure collection of reliable outcomes, PROMs require methodological evaluation prior to utilization in the STS population. Research should prioritize on determining relevant content and subsequently selecting the most suitable PROM for assessment.
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".