Evolution of the Surgical Management of Lung Cancer Invading the Spine: A Single Center Experience
Bibliographic record
Abstract
For patients with locally advanced non-small cell lung cancer invading the spine, induction chemoradiotherapy combined with radical en bloc resection is the key to obtaining long-term survival. With time, our operative technique evolved to a two-step surgery as we experienced numerous perioperative complications during one step surgery. The aim of our study was to assess postoperative morbimortality and long-term survival of both techniques. We retrospectively reviewed all patients who underwent en bloc resection for lung cancer invading the spine between October 2012 and June 2020. Every patient underwent induction therapy. Sixteen patients were included: nine patients were operated on with one step surgery, seven patients were operated on with two step interventions. Twenty-five percent of patients had major perioperative complications and 56.2% of patients had major post-operative complications. Patients in the "one step" group tended to have more perioperative complications whereas patients in the "two step" group tended to have more post-operative complications. Overall 3-year survival was 40% in the one-step and 86% in the two-step surgery group. Although our practice has been improved by two-step interventions, post-operative morbidity remains significant. As long term survivals are encouraging, this type of treatment should still be proposed for highly selected patients, in specialized centers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".