NT-05 * PATIENT ACCEPTANCE OF ROUTINE RESAMPLING OF GLIOBLASTOMA: A QUALITATIVE STUDY
Bibliographic record
Abstract
INTRODUCTION: The routine resampling of glioblastoma may be very useful to guide patient care after initial surgery, radiation, and chemotherapy, akin to bone marrow biopsy in leukemia patients. Qualitative research methodology was used to explore the views of patients on this issue. Qualitative research is a form of social sciences investigation, focusing on the experiences of participants in real, not experiment settings. It has not been used much by clinicians, especially neuro-oncologists, but this is slowly changing. METHODS: 30 patients with glioblastoma were recruited from a neuro-oncology clinic in a cancer center serving three adult teaching hospitals. The protocol was passed by the IRB. The study subjects were given semi-structured interviews using an interview guide, and the recorded data assessed using modified thematic analysis and grounded theory. Overarching themes were extracted from the data. RESULTS: Several themes arose - the primary and most important was that routine resampling of glioblastoma was acceptable to most patients. CONCLUSIONS: Using qualitative research methodology, we can learn insights about patients' experience that we cannot learn from quantitative research. This information can help us learn: 1) what our patients are feeling; 2) how we can better talk to them; 3) what issues are important to them to talk about; 4) how we can improve their care and their perception of care; and 5) how we can better conduct clinical research. This can lead to improvement in the quality of our neuro-oncology patients' experience and help facilitate the practice of patient-centered care for brain tumor patients. This particular study demonstrates that routine resampling of glioblastoma is acceptable to patients, and more generically, it represents a novel way of assessing if innovative treatments would be acceptable to the patients they are designed for by directly asking patients for their views.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".