Determining the unmet needs of patients with intracranial meningioma—a qualitative assessment
Bibliographic record
Abstract
BACKGROUND: Meningiomas are the most common primary benign brain neoplasms, but despite their commonality, the supportive needs of this patient population have been overlooked. The aim of this study is to identify unmet needs of meningioma patients, caregivers, and health care providers. METHODS: We adopted a patient-centered approach by using qualitative interviewing with patients diagnosed with a meningioma who have undergone treatment in the last 10 years since the date of their interview. Informal caregivers (family and/or friends) of the patient population and health care providers who are normally involved in the management and care of meningioma patients were also interviewed. Interview transcripts were subjected to thematic analysis. RESULTS: Of the 50 participants interviewed, there were 30 patients, 12 caregivers, and 8 health care professionals. Thematic analysis revealed 4 overarching themes: (1) access to targeted postoperative care, (2) financial struggles for patients and their families, (3) lack of information specific to meningiomas and postsurgical management, and (4) lack of psychosocial support. CONCLUSION: This study identified supportive needs specific to the meningioma patient population, which predominantly falls within the postoperative phase. The postoperative journey of this patient population could potentially extend to the rest of the patient's life, which necessitates resources and information directed to support postoperative recovery and management. The development of directly relevant supportive resources that support meningioma patients in their postoperative recovery is necessary to improve the health-related quality of life in this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".