Screening for Cognitive Impairment in the Neurologist's office (P3.218)
Bibliographic record
Abstract
<OBJECTIVE: To develop/validate a semi-structured interview to screen MS patients for cognitive impairment (CI) in a clinic setting. BACKGROUND: Neuropsychological evaluation can be time consuming and difficult to access. During routine visits, clinicians are not always able to adequately determine if CI is present. METHODS: This interview was designed to guide a discussion regarding five domains of everyday life to determine change in functional ability related to CI. Tasks/scenarios were identified from each domain and explored regarding subjective complaints and feedback received from others, such as a supervisor or family member. Phase 1 (Interview development): 20 MS patients with subjective complaints of CI were interviewed and tested with the MACFIMS battery. Questions regarding the five categories were refined. Prompts regarding suitable tasks for the interviewer were developed. Phase 2 (Interview Validation): A neurologist/MS specialist, MS clinic nurse practitioner or community neurologist used the interview tool on 60 MS patients with subjective complaints of CI, which was objectively assessed by MACFIMS. Bayesian statistics were used to calculate sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: Data from phase 2 is presented. Subjects had a mean age of 44.4 years (SD 10.6), years of education 13.9 (SD 2.9) and were diagnosed with MS 8.9 years (SD 10.0) ago. Most were female (48, 80.0[percnt]), Caucasian (55, 91.7[percnt]) and had RRMS (44, 73.3[percnt]). Median EDSS was 3 (range 0 - 7.0) and 30 (50[percnt]) were taking disease modifying medications. 50 (83.3[percnt]) were impaired on at least one test in the MACFIMS battery, most frequently the SDMT (37, 61.8[percnt]). The interview had a sensitivity of 95[percnt], specificity of 40.0[percnt], PPV 76.0[percnt] and NPV 80.0[percnt]. CONCLUSIONS: This interview tool can be used to screen MS patients for CI in a clinic setting. The study was funded by an unrestricted grant from BiogenIdec.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.009 | 0.003 |
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".