A comparison of questionnaires for assessing physical function in patients with lower extremity bone metastases
Bibliographic record
Abstract
OBJECTIVES: To assess, (i) the degree to which the: PROMIS Physical Function Cancer, PROMIS Neuro-QoL Mobility, Toronto Extremity Salvage Score (TESS), Lower Extremity Function Score (LEFS), and Musculoskeletal Tumor Society score (MSTS), measure physical function; (ii) differences in coverage and reliability; and (iii) difference in completion time. METHODS: One hundred of 115 (87%) patients with lower extremity metastases participated in this prospective study. We used exploratory factor analysis-correlating questionnaires with an underlying trait-to assess if questionnaires measure the same. Coverage was assessed by floor and ceiling effect and reliability by the standard error of measurement (SEM). Completion time was compared using the Friedman test. RESULTS: All questionnaires measured the same concept; demonstrated by high correlations (>0.7). Floor effect was absent, while ceiling effect was present in all, but highest for the PROMIS Neuro-QoL Mobility (7%). The SEM was below the threshold-indicating reliability-over a wide range of ability levels for the PROMIS-Physical Function, TESS, and LEFS. Completion time differed between questionnaires (P < 0.001) and was shortest for the PROMIS questionnaires. CONCLUSIONS: The PROMIS Physical Function is the most useful questionnaire. This is due to its reliability over a wide range of ability levels, validity, brevity, and good coverage. J. Surg. Oncol. 2016;114:691-696. © 2016 Wiley Periodicals, Inc.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".