Evaluating measurement quality and feasibility of neurocognitive screening instruments for adult survivors of out-of-hospital cardiac arrest: A systematic review
Bibliographic record
Abstract
AIM: To systematically review and appraise the measurement properties, interpretability, and feasibility of outcome measurement instruments (OMIs) for screening neurocognitive function among adult survivors of out-of hospital cardiac arrest (OHCA). METHODS: Online databases were searched (MEDLINE, EMBASE, CENTRAL, CINAHL, PsychINFO) from inception - August 23, 2024. Included articles evaluated measurement properties, interpretability (assigning meaning to scores), and/or feasibility (ease of application) of multidomain instruments for screening neurocognitive function among OHCA survivors. Evidence was reviewed according to the Consensus-based Standards for the selection of health status Measurement Instruments (COSMIN) guidelines: Risk of Bias determined study quality; COSMIN criteria for good measurement properties was applied; data were pooled where possible, finally, the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria rated the certainty of evidence. RESULTS: Of 2336 titles and abstracts, 27 articles provided evidence for 13 instruments (MoCA, T-MoCA, Mini MoCA, MMSE, MMSE-ALFI, 3MS, TICS, TICS-m, WASI, IQCODE, IQCODE-CA, SF-16 IQCODE-CA, and CFQ). Only three reported measurement properties for two OMIs: the MoCA and the IQCODE-CA. The MoCA demonstrated high quality evidence of criterion validity across two studies (pooled AUC: 0.80; 95% CI: 0.67 to 0.93). There were inconsistent results for the IQCODE-CA. There was limited evidence of score interpretability and feasibility across all 13 OMIs. CONCLUSIONS: Insufficient evidence of essential measurement properties limits instrument choice. Whilst the MoCA had acceptable criterion validity, evidence of content validity was inadequate, and reliability and responsiveness lacking. Establishing robust evidence to inform screening of neurocognitive function in this population should be prioritized.
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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 | 0.018 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".