The Fast Cognitive Evaluation (FaCE): a screening tool to detect cognitive impairment in patients with cancer
Bibliographic record
Abstract
Abstract Cancer-related cognitive impairment (CRCI) is one of the most concerning conditions experienced by patients living with cancer and has a major impact on their quality of life. Available cognitive assessment tools are too time consuming for day-to-day clinical setting assessments. Importantly, although shorter, screening tools such as the Montreal Cognitive Assessment or the Mini-Mental State Evaluation have demonstrated a ceiling effect in persons with cancer, and thus fail to detect subtle cognitive changes expected in patients with CRCI. This study addresses this lack of cognitive screening tools by developing a novel tool, the Fast Cognitive Evaluation (FaCE). A population of 245 patients with 11 types of cancer at different illness and treatment time-points was enrolled for the analysis. FaCE was developed using Rasch Measurement Theory, a model that establishes the conditions for a measurement tool to be considered a rating scale. FaCE shows excellent psychometric properties. The population size was large enough to test the set of items (item-reliability-index=0.96). Person-reliability (0.65) and person-separation (1.37) indexes indicate excellent internal consistency. FaCE’s scale is accurate (reliable) with high discriminant ability between cognitive levels. Within the average testing time of five minutes, FaCE assesses the main cognitive domains affected in CRCI. FaCE is a rapid, reliable, and sensitive tool for detecting even minimal cognitive changes over time. This can contribute to early and appropriate interventions for better quality of life in patients with CRCI. In addition, FaCE could be used as a measurement tool in research exploring cognitive disorders in cancer survivors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".