P.161 Management of recurrent glioblastoma multiforme: An inter-observer variability study
Bibliographic record
Abstract
Background: A significant proportion of glioblastoma multiforme (GBM) patients are considered for repeat resection, but evidence regarding best management remains elusive. Methods: An electronic portfolio of MR images of 37 cases of pathologically confirmed recurrent GBM with an accompanying clinical vignette was constructed. Surgical responders from various countries, training backgrounds, and years’ experience were asked for each case to select: their chosen management (repeat surgery, chemotherapy, radiation, or conservative), confidence in recommended management, and whether they would include the patient in a randomized trial that gave a 50% chance of re-operation. Responses were evaluated with kappa statistics and values interpreted according to Landis and Koch (0–0.2, slight; 0.21–0.4, fair; 0.41–0.6, moderate; 0.61–0.8, substantial; 0.81-1.0 perfect agreement). Results: 26 surgeons responded to the survey. Agreement regarding best management of recurrent GBM was slight, even when management options were dichotomized (repeat surgery vs. all others) (k=0.198 (95%CI 0.133-0.276). Country of practice, years’ experience, and training background did not improve agreement. Responders were willing to include more than 70% of patients in a randomized trial. Conclusions: Only slight agreement exists regarding the question of re-operation for patients with recurrent GBM. This supports the need for a randomized controlled trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".