Fractionated Radiotherapy After Gross Total Resection of Clival Chordoma: A Systematic Review of Survival Outcomes
Bibliographic record
Abstract
BACKGROUND: Current treatment guidelines for clival chordomas recommend surgical resection followed by high-dose radiotherapy (RT). However, in patients in whom gross total resection (GTR) is achieved, the benefits of additional RT remain unclear. OBJECTIVE: To investigate whether RT offers any benefit to progression-free survival (PFS) in patients undergoing GTR of clival chordoma by performing a systematic review of all currently published literature. METHODS: A total of 5 databases were searched to include all studies providing data on GTR ± RT for clival chordomas (January 1990-June 2021). Qualitative assessment was performed with Newcastle-Ottawa Scale guidelines for assessing quality of nonrandomized studies. Statistical analysis using individualized patient data of PFS was performed. RESULTS: The systematic search yielded 2979 studies, weaned to 22 full-text articles containing 108 patients. All patients underwent GTR of clival chordoma, with 46 (43%) patients receiving adjuvant RT. Mean PFS for RT patients was 31.09 months (IQR: 12.25-37.75) vs 54.92 months (IQR: 14.00-85.75) in non-RT patients. Overall, RT did not increase PFS (HR 0.320, P = .069) to a value that achieved statistical significance. Stratifying by photon therapy vs particle beam therapy yielded no statistically significant benefit for particle beam therapy for PFS ( P = .300). Of patients with age ≥65 years, RT did not improve outcomes to statistical significance for PFS (HR 0.450, P = .481). Patients age ≥65 years had lower PFS on both bivariate analysis (HR 3.708, P = .007) and multivariate analysis (HR 3.322, P = .018). CONCLUSION: After achieving GTR of clival chordoma, fractionated RT offers unclear benefit upon survival outcomes.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".