The Role of Frailty and Sarcopenia in Predicting Major Adverse Events, Length of Stay and Reoperation Following <i>En Bloc</i> Resection of Primary Tumours of the Spine
Bibliographic record
Abstract
Study Design Retrospective observational cohort study. Objective En bloc resection for primary tumours of the spine is associated with a high rate of adverse events (AEs). The objective was to explore the relationship between frailty/sarcopenia and major perioperative AEs, length of stay (LOS), and unplanned reoperation following en bloc resection of primary spinal tumours. Methods This is a unicentre study consisting of adult patients undergoing en bloc resection for a primary spine tumor. Frailty was calculated with the modified frailty index (mFI) and spine tumour frailty index (STFI). Sarcopenia was quantified with the total psoas area/vertebral body area ratio (TPA/VB) at L3 and L4. Univariable regression analysis was used to quantify the association between frailty/sarcopenia and major perioperative AEs, LOS and unplanned reoperation. Results 95 patients met the inclusion criteria. The mFI and STFI identified a frailty prevalence of 3% and 18%. Mean CT TPA/VB ratios were 1.47 (SD ± .05) and 1.83 (SD ± .06) at L3 and L4. Inter-observer reliability was .93 and .99 for CT and MRI L3 and L4 TPA/VB ratios. Unadjusted analysis demonstrated sarcopenia and mFI did not predict perioperative AEs, LOS or unplanned reoperation. Frailty defined by an STFI score ≥2 predicted unplanned reoperation for surgical site infection (SSI) ( P < .05). Conclusions The STFI was only associated with unplanned reoperation for SSI on unadjusted analysis, while the mFI and sarcopenia were not predictive of any outcome. Further studies are needed to investigate the relationship between frailty, sarcopenia and perioperative outcomes following en bloc resection of primary spinal tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".