From hope to loss and back again: loved ones’ experience with glioblastoma in the spatial heterogeneity challenge
Bibliographic record
Abstract
BACKGROUND: Glioblastoma (GBM) is an aggressive primary brain tumour that carries a great deal of symptom burden, placing significant stress on caregivers. The purpose of this current study is to capture realities of bereaved loved ones of patients with GBM throughout the illness journey and to understand family beliefs and experiences regarding a post-mortem whole brain analysis study, the Spatial Heterogeneity Challenge (SHC). METHODS: This qualitative description study utilized semi-structured interviews with 16 bereaved loved ones of previous SHC donors. Data was analyzed using thematic analysis. RESULTS: Experiences were temporally categorized into phases of the illness journey (diagnosis, standard therapy, 'honeymoon period', clinical trials, end of life, and donation). Within these categories, participants reported inadequate support in caring for their loved one and accessing clinical trials. Regarding the SHC, participants felt proud of the patient's participation, and reported alleviated stress and meaning provided to an otherwise devastating loss. CONCLUSIONS: The illness journey of patients with GBM and their families is unique from other cancers and requires additional support from our healthcare system with a streamlined approach to care. Providing the opportunity to participate in studies like the SHC after death allows families to gain meaning from this devastating experience.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".