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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".