A Critical Appraisal of the Application of Frailty and Sarcopenia in the Spinal Oncology Population
Bibliographic record
Abstract
Study Design Systematic review and clinimetric analysis. Objectives Frailty and sarcopenia predict worse surgical outcomes among spinal degenerative and deformity-related populations; this association is less clear in the context of spinal oncology. Here, we sought to identify frailty and sarcopenia tools applied in spinal oncology and appraise their clinimetric properties. Methods A systematic review was conducted from January 1 st , 2000, until June 2022. Study characteristics, frailty tools, and measures of sarcopenia were recorded. Component domains, individual items, cut-off values, and measurement techniques were collected. Clinimetric assessment was performed according to Consensus-based Standards for Health Measurement Instruments. Results Twenty-two studies were included (42 514 patients). Seventeen studies utilized 6 frailty tools; the three most employed were the Metastatic Spine tumor Frailty Index (MSTFI), Modified Frailty Index-11 (mFI-11), and the mFI-5. Eight studies utilized measures of sarcopenia; the three most common were the L3-Total Psoas Area (TPA)/Vertebral Body Area (VBA), L3-TPA/Height 2 , and L3-Spinal Muscle Index (L3-Cross-Sectional Muscle Area/Height 2 ). Frailty and sarcopenia measures lacked or had uncertain content and construct validity. Frailty measures were objective except the Johns-Hopkins Adjusted Clinical Groups. All tools were feasible except the Hospital Frailty Risk Score (HFRS). Positive predictive validity was observed for the HFRS and in select studies employing the mFI-5, MSTFI, and L3-TPA/VBA. All frailty tools had floor or ceiling effects. Conclusions Existing tools for evaluating frailty and sarcopenia among patients undergoing surgery for spinal tumors have poor clinimetric properties. Here, we provide a pragmatic approach to utilizing existing frailty and sarcopenia tools, until more clinimetrically robust instruments are developed.
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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 | 0.083 | 0.290 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.034 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".