Are neurosurgeons prepared to electively resample glioblastoma in patients without symptomatic relapse? A qualitative study
Bibliographic record
Abstract
Background This is a qualitative study designed to examine neurosurgeons' and neuro-oncologists' perceptions of resampling surgery for glioblastoma multiforme electively, post-therapy or at asymptomatic relapse. Methods Twenty-six neurosurgeons, three radiation oncologists and one neuro-oncologist were selected using convenience sampling and interviewed. Participants were presented with hypothetical scenarios in which resampling surgery was offered within a clinical trial and another in which the surgery was offered on a routine basis. Results Over half of the participants were interested in doing this within a clinical trial. About a quarter of the participants would be willing to consider routine resampling surgery if: (1) a resection were done rather than a simple biopsy; (2) they could wait until the patient becomes symptomatic and (3) there was a preliminary in vitro study with existing tumour samples to be able to offer patients some trial drugs. The remaining quarter of participants was entirely against the trial. Participants also expressed concerns about resource allocation, financial barriers, possibilities of patient coercion and the fear of patients' inability to offer true informed consent. Conclusion Overall, if surgeons are convinced of the benefits of the trial from their information from scientists, and they feel that patients are providing truly informed consent, then the majority would be willing to consider performing the surgery. Many surgeons would still feel uncomfortable with the procedure unless they are able to offer the patient some benefit from the procedure such that the risk to benefit ratio is balanced.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".