A comparison of the modified Tokuhashi and Tomita scores in determining prognosis for patients afflicted with spinal metastasis
Bibliographic record
Abstract
BACKGROUND: The prognosis of patients with spinal metastasis is not very promising and hard to predict. It is for this reason that scoring systems, such as the modified Tokuhashi and Tomita scores, have been created. We sought to determine the effectiveness of these scores in predicting patient survival. METHODS: We retrospectively reviewed the data of all patients treated for spinal metastasis between March 2003 and March 2012 in our centre. We computed the Tokuhashi and Tomita scores and compared them with documented patient survival. The 2 scores were also compared with one another. RESULTS: We identified 128 patients with spinal metastasis. The average survival of patients with predicted poor, average and good prognosis was 5, 17 and 25 months, respectively for the modified Tokuhashi score and 3, 16 and 19 months, respectively, for the Tomita score. Poor, average and good prognosis predictions differed significantly from one another for all 3 categories for the Tokuhashi score (all p < 0.05). There was no significant difference in the moderate and good prognoses for the Tomita score (p = 0.15). When comparing both scores, we obtained a weighted κ of 0.4489 (standard deviation 0.0568, 95% confidence interval 0.3376-0.5602), demonstrating moderate agreement between scores. CONCLUSION: Both scores have merit for use in a clinical setting and can be used as tools to help determine treatment choice. The modified Tokuhashi score had better accuracy in determining actual survival.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".