Anxiety and depression in patients with intracranial meningioma: a mixed methods analysis
Bibliographic record
Abstract
BACKGROUND: While diagnosis with a high-grade intracranial tumor is known to be associated with increased psychosocial burden, the burdens associated with meningioma are less well described. This study aimed to investigate the mental health burden in patients with meningiomas who have undergone surgical resection or serial observation, so as to identify and enhance awareness of gaps in care. METHODS: The Hospital Anxiety and Depression Scale (HADS) was administered to participants. Fisher's Exact tests were performed to evaluate frequency distributions and t-tests were applied to compare postoperative and non-surgical patients' HADS scores. Semi-structured interviews were completed on a subset of participants. Thematic analysis of interviews identified emerging themes. RESULTS: Thirty patients with intracranial meningiomas met inclusion criteria. The cohort's mean age was 56.01 years and 66.67% were women (n = 20). Fourteen underwent surgery; sixteen were treated conservatively with observation. The average time since diagnosis of the sample was 37.6 months. Prevalence of mild to severe symptoms of anxiety was 28.6% amongst surgical management patients and 50% for active surveillance patients (p = 0.325). The prevalence of mild to severe symptoms of depression was 7.14% amongst surgical management patients and 6.25% for active surveillance patients (p = 0.533). Emerging themes from eight interviews reveal the influence of resilience, uncertainty and time, social support, interactions with medical experts, and difficulties during recovery on mental health. CONCLUSION: The findings from the present study reveal that patients with meningiomas experience a significant mental health burden, illustrating the need for enhanced patient-centred care focusing on mental health.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".