Can cognitive function tests discriminate between patients with glioma and healthy controls prior to treatment? A systematic review
Bibliographic record
Abstract
BACKGROUND: Brain tumours affect 7 per 100,000 people in the UK, glioma being most prevalent, with only 12% five-year survival rates and devastating impacts. Primary care triage tools could facilitate earlier detection of glioma. One option for triage is cognitive function testing. The aim of this systematic review was to determine if cognitive function tests can discriminate between patients with glioma and healthy controls, and their potential suitability for primary care use. METHODS: Studies were included that conducted cognitive function tests with adult patients with glioma, prior to treatment, compared to healthy controls. Two independent researchers performed screening and data extraction. The primary outcome explored test discrimination between people with glioma and healthy controls. RESULTS: Seventeen studies were identified. Findings indicated multiple cognitive function and language function have potential discriminatory capacity between patients with glioma and healthy controls. Over half of cognitive function tests measuring multiple cognitive functions (59%, n = 17) and language function (54%, n = 30) found significant differences between patients with glioma and healthy controls with medium or large effect size. The Montreal Cognitive Assessment has short test duration, high feasibility and acceptability, suggesting potential primary care suitability. Further acceptability and feasibility studies are needed for other potential tests. CONCLUSIONS: Acknowledging high heterogeneity of included studies, this review suggests tests of multiple cognitive functions or language could support primary care practitioners with decision-making for urgent neuroimaging referral. However, interpretations should be treated with caution and the applicability to primary care requires further exploration. Prospero registration number: CRD42023408671.
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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 | 0.011 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".