Defining cognitive impairment in amyotrophic lateral sclerosis: an evaluation of empirical approaches
Bibliographic record
Abstract
Abstract Importance Amyotrophic lateral sclerosis (ALS) is a multi-system disorder characterized primarily by motor neuron degeneration, but may be accompanied by cognitive dysfunction. Statistically appropriate criteria for establishing cognitive impairment (CI) in ALS are lacking. Objective Define thresholds for CI in ALS using quantile regression (QR) that accounts for age and education in a North American (NAmer) cohort. Design QR of cross-sectional data from a multi-center NAmer cohort of healthy adults was used to model the 5 th percentile of cognitive scores on the Edinburgh Cognitive and Behavioral ALS Screen (ECAS). The QR approach was compared to a traditional 2 standard deviation (SD) cut-off approach using the same NAmer cohort (2SD-NAmer) and to existing UK-based normative data derived using the 2SD approach (2SD-UK) to assess the impact of cohort selection and statistical model in identifying CI ALS patients. Participants 269 healthy adults from NAmer, recruited by the University of Pennsylvania (PENN; N=82), the University of Miami through the CRiALS study (CRiALS; N=40), and the Canadian ALS Neuroimaging Consortium (CALSNIC; N=147) were included to establish ECAS thresholds for defining CI. We then evaluated the frequency of CI in 182 ALS patients from PENN. Main Outcomes We defined two new sets of normative thresholds, based on NAmer heathy adult performance, for each ECAS domain score and the composite scores using QR and 2SD statistical approaches. We then applied the 2SD-NAmer and QR-NAmer, as well as the previously established and widely-used 2SD-UK, thresholds to evaluate the frequency of CI in ALS patients. Results QR-NAmer models revealed that increased age and reduced educational attainment negatively impact cognitive performance on the ECAS. Based on the QR-NAmer normative cutoffs, the prevalence of CI in the 182 PENN ALS patients was 15.9% for ECAS ALS-Specific and 15.4% for ECAS Total. These estimates are more conservative than estimates ranging from 15.4%-34.6% impaired based on 2SD approaches. Conclusions and Relevance This report establishes normative thresholds for using ECAS to identify whether ALS patients in the NAmer population have CI. The choice of statistical method and normative cohort has a substantial impact on defining CI in ALS. Key Points Question How to define cognitive impairment (CI) in amyotrophic lateral sclerosis (ALS) using the Edinburgh Cognitive and Behavioral ALS Screen (ECAS)? Findings Age- and education-adjusted quantile regression (QR) yields thresholds for defining CI that differ meaningfully from those derived from parametric methods without age- and education-adjustment. Thresholds also differ between UK and North American cohorts. Applying our North American-based QR norms to an American ALS cohort at a single center identified CI based on ECAS performance in ∼16% patients, compared to 15.4%-34.6% patients using other approaches. Meaning The choice of statistical method and normative cohort has a substantial impact on defining CI in ALS.
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 | 0.189 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".