11 ONLINE SURVEY OF PATIENT EXPERIENCES RECEIVING GLIOMA DIAGNOSES
Bibliographic record
Abstract
Abstract INTRODUCTION Accounting for 80% of all primary brain cancers, gliomas are the most likely of all cancer types to be diagnosed during an emergency hospital admission following sudden symptom deterioration. This, coupled with the inherent complexity of histomolecular glioma classification, can exacerbate patient distress during the diagnostic period. Our patient-led survey project aimed to explore patient experiences receiving glioma diagnoses in an international cohort. METHODS Survey responses were collected from brain cancer patients and family members from January 2019 to March 2024 using an online form. Demographic and clinical information was collected in conjunction with responses to open-ended questions regarding patients’ emotional experiences. Sentiment, content, and thematic analyses of these responses were performed. RESULTS Fifty-four glioma patients (34, 63%) and/or family members (20, 37%) responded to the questionnaire. Fifty-six percent of the patients were female, with a median age at diagnosis of 40 (IQR: 29–52) years. Most respondents were from the USA (60%) or Canada (11%). The majority of diagnoses were of glioblastoma (46%) or grade 4 astrocytoma (26%). Prominent sentiments expressed surrounding receipt of the diagnosis included a sense of shock and loss of autonomy. Responses regarding interactions with the healthcare system received mixed sentiment scores, communicating both gratitude towards clinicians and frustration with the complexity of navigating multidisciplinary care. CONCLUSION Our results highlight opportunities to ease patient anxiety around the time of glioma diagnosis through clear communication between providers, patients and families, and between members of the healthcare team.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".