P.151 A critical appraisal of the application of frailty and sarcopenia in the spinal oncology population
Bibliographic record
Abstract
Background: 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 identified frailty and sarcopenia tools applied in spinal oncology and appraised their clinimetric properties. Methods: A systematic review was conducted from January 1 st , 2000, until June 2022. Study characteristics, frailty tools, measures of sarcopenia, 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). The three most employed frailty tools were the Metastatic Spine tumor Frailty Index (MSTFI), Modified Frailty Index-11 (mFI-11), and the mFI-5. The three most common sarcopenia measures 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 content and construct validity. Positive predictive validity was observed in select studies employing the HFRS, mFI-5, MSTFI, and L3-TPA/VBA. All frailty tools had floor or ceiling effects. Conclusions: Existing tools for evaluating frailty and sarcopenia in surgical spine oncology 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 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".