Treatment-Related Decisions in Malignant Gliomas: A Feasibility Study
Bibliographic record
Abstract
Background: Glioma patients make frequent decisions regarding treatment and end-of-life care despite cognitive limitations. We evaluated the feasibility of incorporating the Macarthur Competence Assessment Tool for Treatment (MacCAT-T) to assess decision-making ability in glioma patients. Methods: High-grade glioma patients were consented to an IRB-approved prospective study at one of three treatment decision time points. Patients completed the Montreal Cognitive Assessment (MoCA) and providers informally assessed patient decision-making ability based on neurologic examination. The MacCAT-T, designed to assess patient decision-making domains, was administered by a research assistant. MoCA, provider assessment, and MacCAT-T results were compared to determine whether the MacCAT-T provided additional information. To assess feasibility, we measured administration time and obtained qualitative patient feedback. Results: Eleven patients (median age = 68 years, median Karnofsky Performance Status [KPS] = 80–90) were enrolled. MacCAT-T administration averaged 18.5 minutes. Ninety percent of patients reported “increased knowledge of their treatment options” after taking the MacCAT-T. Clinicians deemed 10 patients to possess sufficient decision-making ability, yet, 6 of them demonstrated impairments in reasoning on the MacCAT-T. Seven patients yielded discordant MOCA and MacCAT-T data, five patients with MOCA score ≥26 showed qualitative MacCAT-T impairments in Reasoning and five patients who scored <21 were within nonimpaired ranges for three of four decision-making domains. Conclusion: MacCAT-T administration was feasible and informative to patients but findings were discordant from MOCA and informal provider assessments. The MacCAT-T may help in identifying mild Reasoning impairments related to patients' initial treatment decisions and should be studied further to determine its role in clinical practice.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".