P.133 A retrospective analysis of the clinical utility of the Tokuhashi scale, and its impact in surgical management of spinal metastatic disease
Bibliographic record
Abstract
Background: The evaluation of patients presenting with spinal metastatic disease is often challenging. The Tokuhashi scale intends to facilitate this process. We conducted this study to investigate its clinical utility in surgical-decision making in patients with spinal metastasis. Methods: The oncology database was used to allocate 285 patients with spinal metastasis between 2010 and 2015. The Tokuhashi scale components were determined from a chart review. Results: Based on the Tokuhashi scale, there was 69.1% in the non-operative/radiation group (group 1), 23.2% in the palliative/excisional surgical group (group2) and 7.7% in the surgical group (group 3). Using Kaplan-Meiers estimate, survival time was significantly different across the three groups with means 232.8±30.8, 352.3±49.2 and 568.3±206.1 days, respectively. A significantly higher proportion of patients (84.6%) were treated non-surgically in group 1, compared to 45.5% in group 3 (X2=19.5, P<0.001). However, there was no correlation between the type of surgical interventions (i.e. instrumented decompression, decompression alone, percutaneous vertebral augmentation and instrumented vertebral augmentation) and the Tokuhashi score. Conclusions: This review illustrates the utility of the Tokuhashi scale in predicting survival. However, it does not address the new role of emerging different surgical strategies for the treatment of spinal metastasis and lacks information concerning spinal instability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".