Trends in functional outcome measures in orthopedic oncology
Bibliographic record
Abstract
The purpose of this study was to identify trends in the use of functional outcome measures within orthopedic oncology. The search engine, PubMed, was reviewed for all articles over an 11-year period from 2011 to 2021 from five major journals that publish in the field of orthopedic oncology. The functional outcome measures used in the articles were recorded along with study date, study design, clinical topic/pathology, and level of evidence. Out of 5968 musculoskeletal tumor-focused articles reviewed, 293 (4.9%) included at least one outcome measure. A total of 28 different outcome tools were identified. The most popular were Musculoskeletal Tumor Society (MSTS) score (61.1%) and Toronto Extremity Salvage (TESS) score (14.0%), followed by 36-Item Short Form Survey (SF-36) (4.1%) and Patient-Reported Outcomes Measurement Information System (PROMIS) (3.8%). The use of MSTS scores decreased by 0.7% each year, whereas PROMIS increased by 1.2% each year. Seventy-four articles used more than one outcome measure. Of these 74 articles, 61 had the MSTS as one of the outcome measures. Orthopedic oncology utilizes functional outcome measures less commonly in comparison to other orthopedic subspecialties. However, this may be due in large part to orthopedic oncologists putting more emphasis on outcomes such as local recurrence, implant failure, and mortality. MSTS score is the most widely used functional outcome measure, but the utilization of PROMIS has increased recently, and could be the next step in evaluating outcomes in orthopedic oncology as it is patient-derived rather than physician-derived.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".