A pragmatic tool to screen for pre‐transplant cognitive impairment among potential candidates for liver transplant
Bibliographic record
Abstract
INTRODUCTION: Cognitive impairment (CI) among liver transplant (LT) candidates is associated with increased risk of waitlist mortality and inferior outcomes. While formal neurocognitive evaluation is the gold standard for CI diagnosis, the Montreal Cognitive Assessment (MoCA) is often used for first-line cognitive screening. However, MoCA requires specialized training and may be too lengthy for a busy evaluation appointment. An alternate approach may be the Quick Dementia Rating System (QDRS), which is patient- and informant-based and can be administered quickly. We compared potential LT candidates identified by MoCA and QDRS as potentially benefiting from further formal cognitive evaluation. METHODS: We identified 46 potential LT candidates enrolled at a single center of a prospective, observational cohort study who were administered MoCA and QDRS during transplant evaluation (12/2021-12/2022). Scores were dichotomized as (1) normal versus abnormal and (2) normal/mild impairment versus more-than-mild impairment. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of QDRS compared to MoCA. RESULTS: By MoCA, this population had a prevalence of 48% normal cognition, 48% mild, 4% moderate, and 0% severe impairment. This was categorized as 96% normal/mild and 4% more-than-mild impairment. When comparing to MoCA cognitive screening, QDRS had a sensitivity of 61%, specificity of 56%, NPV of 56%, and PPV of 61%. When identifying more-than-mild impairment, QDRS had a sensitivity of 100%, specificity of 73%, NPV of 100%, and PPV of 10%. CONCLUSION: The high sensitivity and NPV of QDRS in identifying more-than-mild impairment suggests it could identify potential LT candidates who would benefit from further formal cognitive evaluation. The ability to administer QDRS quickly and remotely makes it a pragmatic option for pre-transplant screening.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".