Assessing Cognitive Outcomes in Coma Survivors: A Literature Review
Bibliographic record
Abstract
(1) Background: Although cognitive impairments in coma survivors are common, methods of measuring long-term cognitive outcomes in this population are inconsistent, precluding the development of a strong evidence-base to support clinical decision making. In this literature review, we identify and characterize the measures used to track cognitive recovery in coma survivors to data. (2) Methods: We extracted the instrument used for cognitive assessment, the cognitive domains assessed, methods administration and scoring, and timing of assessment from 134 of 996 screened records. (3) Results: A total of 133 unique cognitive tests and cognitive testing batteries were identified, with 97 cognitive instruments used in less than three articles. The instruments assessed 20 different cognitive domains, with 73 articles also using tests that assess general "cognitive ability". Cognitive instruments ranged from subjective assessments to comprehensive cognitive batteries. There were inconsistent points of reference for the timing of assessment across studies, with few studies repeating assessments at more than one time point, and arbitrary time intervals between tests. (4) Conclusions: Overall, this review illustrates the enormous disparity between studies that track cognitive outcome in coma survivors, and the need for a systematic, patient-accessible method of assessing cognitive functioning in future studies with this population.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".