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.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".