Use of the Montreal Cognitive Assessment as an Early Indicator of Tumor Progression in Patient with Stage III and IV Gliomas
Bibliographic record
Abstract
Patients diagnosed with high grade glioma have a short life expectancy due to rapid progression of disease following and/or during treatment. Magnetic resonance imaging (MRI) is the primary method of surveying tumor progression, but is costly, lengthy in duration and often uncomfortable for the patient. An alternative to MRI that is cost efficient and patient friendly is of great interest to the medical community. If this alternative could also provide advanced notification of disease progression, then this patient population would have the opportunity for earlier treatment and the potential for greater efficacy. To pursue this concept, we assessed whether the Montreal Cognitive Assessment (MoCA) could be that MRI alternative, potentially providing an early identifier of disease progression for the high grade glioma population. We retrospectively assessed a variety of medical and surgical data points, in conjunction with the MoCA scores for individuals with a high grade glioma diagnosis who received surgery and/or biopsy with radiation treatment and had at least one instance of disease progression. Of the 128 subjects intended to fulfill our sample size requirement, only 5 subjects qualified for enrollment. Our statistical tests were greatly impacted by this unfortunate circumstance and because of this we were not able to support the MoCA as hypothesized because the results did not reach the level of statistical significance. We have identified many interesting trends, but without an appropriate sample size these cannot be validated. We hope the study concept and design will provide the basis for future research that can build upon our hypothesis and provide a definite answer.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".