Malignant brain tumor diagnosis in Manitoba: understanding patient and caregiver perspectives
Bibliographic record
Abstract
Introduction: About 55,000 Canadians are living with a brain tumor today (1). Brain tumor diagnoses are preceded by the onset of headaches, nausea, vomiting, and/or seizures (2). Malignant brain tumors have a poor prognosis and are frequently associated with neurocognitive deficits. Within the first eight months after a new malignant brain tumor diagnosis, 15-20% of patients develop a depressive disorder (3,4). The capability of patients to cope and understand a brain tumor diagnosis is dependent on several factors, including their physical, cognitive and psychosocial health (5,6). In this study, we sought to determine which factors contribute to newly diagnosed malignant brain tumor patient and caregiver needs after diagnosis. Methods: We conducted semi-structured interviews with two newly diagnosed malignant brain tumor patients and their caregivers to qualitatively explore both the patient and caregivers’ perspective about the diagnosis before and after tumor resection at the Health Sciences Centre (HSC). The software program NVivo was used to help analyze and code central themes within the interviews. Results: Our findings resulted in 3 central themes: (1) Information preferences in newly diagnosed malignant brain tumor patients vs. caregivers, (2) Patients managing family, changing quality of life, and healthcare provider visits, and (3) Caregiver responsibility. Our results demonstrated that patients and caregivers differ in their care needs and wants during the diagnostic phase of a malignant brain tumor. Conclusions: These interviews show how complex patient and caregiver needs are how difficult they can be to address with a new brain tumor diagnosis. For both newly diagnosed malignant brain tumor patients and caregivers, quality of life changes dramatically. Screening models aimed at identifying patient and caregiver needs would be helpful to ensure better health-related quality of life (HR-QoL) in brain tumor patients and their caregivers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".