Clinical Uncertainty and Equipoise in the Management of Recurrent Glioblastoma
Bibliographic record
Abstract
BACKGROUND: A significant proportion of glioblastoma (GBM) patients are considered for repeat resection, but evidence regarding best management remains elusive. Our aim was to measure the degree of clinical uncertainty regarding reoperation for patients with recurrent GBM. METHODS: We first performed a systematic review of agreement studies examining the question of repeat resection for recurrent GBM. An electronic portfolio of 37 pathologically confirmed recurrent GBM patients including pertinent magnetic resonance images and clinical information was assembled. To measure clinical uncertainty, 26 neurosurgeons from various countries, training backgrounds, and years' experience were asked to select best management (repeat surgery, other nonsurgical management, or conservative), confidence in recommended management, and whether they would include the patient in a randomized trial comparing surgery with nonsurgical options. Agreement was evaluated using κ statistics. RESULTS: The literature review did not reveal previous agreement studies examining the question. In our study, agreement regarding best management of recurrent GBM was slight, even when management options were dichotomized (repeat surgery vs. other options; κ=0.198 [95% confidence interval: 0.133-0.276]). Country of practice, years' experience, and training background did not change results. Disagreement and clinical uncertainty were more pronounced within clinicians with (κ=0.167 [0.055-0.314]) than clinicians without neuro-oncology fellowship training (κ=0.601 [0.556-0.646]). A majority (51%) of responders were willing to include the patient in a randomized trial comparing repeat surgery with nonsurgical alternatives in 26/37 (69%) of cases. CONCLUSION: There is sufficient uncertainty and equipoise regarding the question of reoperation for patients with recurrent glioblastoma to support the need for a randomized controlled trial.
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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 | 0.258 | 0.540 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".