Combined use of the Montreal Cognitive Assessment and Symbol Digit Modalities Test improves neurocognitive screening accuracy after cardiac arrest: A validation sub-study of the TTM2 trial
Bibliographic record
Abstract
AIM: To assess the merit of clinical assessment tools in a neurocognitive screening following out-of-hospital cardiac arrest (OHCA). METHODS: The neurocognitive screening that was evaluated included the performance-based Montreal Cognitive Assessment (MoCA) and Symbol Digit Modalities Test (SDMT), the patient-reported Two Simple Questions (TSQ) and the observer-reported Informant Questionnaire on Cognitive Decline in the Elderly-Cardiac Arrest (IQCODE-CA). These instruments were administered at 6-months in the Targeted Hypothermia versus Targeted Normothermia after Out-of-Hospital Cardiac Arrest (TTM2) trial. We used a comprehensive neuropsychological test battery from a TTM2 trial sub-study as a gold standard to evaluate the sensitivity and specificity of the neurocognitive screening. RESULTS: In our cohort of 108 OHCA survivors (median age = 62, 88% male), the most favourable cut-off scores were: MoCA < 26; SDMT z ≤ -1; IQCODE-CA ≥ 3.04. The MoCA (sensitivity 0.64, specificity 0.85) and SDMT (sensitivity 0.59, specificity 0.83) had a higher classification accuracy than the TSQ (sensitivity 0.28, specificity 0.74) and IQCODE-CA (sensitivity 0.42, specificity 0.60). When using the cut-points for MoCA or SDMT in combination to identify neurocognitive impairment, sensitivity improved (0.81, specificity 0.74), area under the curve = 0.77, 95% CI [0.69, 0.85]. The most common unidentified impairments were within the episodic memory and executive functions domains, with fewer false negative cases on the MoCA or SDMT combined. CONCLUSION: The MoCA and SDMT have acceptable diagnostic accuracy for screening for neurocognitive impairment in an OHCA population, and when used in combination the sensitivity improves. Patient and observer-reports correspond poorly with neurocognitive performance. CLINICALTRIALS: gov Identifier: NCT03543371.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".