Ecological Momentary Mobile Cognitive Screening for Cancer-Related Cognitive Impairment Among Patients With Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Accessible, real-time cancer-related cognitive impairment (CRCI) assessments remain limited. OBJECTIVES: The aims of this study were to develop and validate the Single Item Mobile Cognitive Screening (SIM-Cog Screening) tool to detect CRCI, establish its concurrent validity and test-retest reliability, and assess feasibility through momentary assessments. METHODS: A secondary analysis was conducted on data from 175 newly diagnosed patients with non-small cell lung cancer. Participants completed in-person assessments, including the Montreal Cognitive Assessment Hong Kong version 5-Min Protocol and Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog) measures, and a mobile survey with SIM-Cog Screening and ratings of anxiety, depression, fatigue, pain, and sleep disturbance. A subgroup of 10 participants completed the ecological mobile survey twice daily for 7 days. Receiver operating characteristic curve analysis used the Montreal Cognitive Assessment Hong Kong version 5-Min Protocol and FACT-Cog measures as reference standards. RESULTS: The receiver operating characteristic curve analysis revealed the area under the curve values of 0.928 when referenced against the FACT-Cog Perceived Cognitive Impairment-18. The optimal cutoff score of 4 on the FACT-Cog Perceived Cognitive Impairment-18 yielded a sensitivity of 88% and a specificity of 83.7%. Single Item Mobile Cognitive Screening scores correlated with measures of anxiety, depression, fatigue, and sleep disturbance. The test-retest reliability coefficient was 0.724, and participants reported high satisfaction with the ecological mobile survey. CONCLUSIONS: This brief, ecological mobile cognitive screening measure can transform how CRCI is detected, monitored, and managed, enabling timely interventions. IMPLICATIONS FOR PRACTICE: Utilizing tools like SIM-Cog Screening can facilitate timely interventions tailored to individual patient needs, ultimately enhancing quality of life and treatment outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".