Feasibility of achieving planned surgical margins in primary spine tumor: a PTRON study
Bibliographic record
Abstract
OBJECTIVE: Oncological resection of primary spine tumors is associated with lower recurrence rates. However, even in the most experienced hands, the execution of a meticulously drafted plan sometimes fails. The objectives of this study were to determine how successful surgical teams are at achieving planned surgical margins and how successful surgeons are in intraoperatively assessing tumor margins. The secondary objective was to identify factors associated with successful execution of planned resection. METHODS: The Primary Tumor Research and Outcomes Network (PTRON) is a multicenter international prospective registry for the management of primary tumors of the spine. Using this registry, the authors compared 1) the planned surgical margin and 2) the intraoperative assessment of the margin by the surgeon with the postoperative assessment of the margin by the pathologist. Univariate analysis was used to assess whether factors such as histology, size, location, previous radiotherapy, and revision surgery were associated with successful execution of the planned margins. RESULTS: Three hundred patients were included. The surgical plan was successfully achieved in 224 (74.7%) patients. The surgeon correctly assessed the intraoperative margins, as reported in the final assessment by the pathologist, in 239 (79.7%) patients. On univariate analysis, no factor had a statistically significant influence on successful achievement of planned margins. CONCLUSIONS: In high-volume cancer centers around the world, planned surgical margins can be achieved in approximately 75% of cases. The morbidity of the proposed intervention must be balanced with the expected success rate in order to optimize patient management and surgical decision-making.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".