Cog-First: standardization of a tablet-based self-administered cognitive screening
Bibliographic record
Abstract
BACKGROUND: Acquired brain injury can lead to subtle cognitive disorders that can be challenging to detect albeit impacting patients' long-term functional prognosis. Cog-First has been developed as a tablet-based self-administered cognitive screening tool to assess executive function, memory and attention in approximately 20 minutes, in the acute phase following brain injury. AIM: The aim of this study was to establish reliable normative data for Cog-First to enable meaningful comparisons between patients and a reference population. DESIGN: Cross-sectional study. SETTING: This study was conducted at the PRISME platform of Paris Brain Institute. POPULATION: Four hundred and six healthy French-speaking healthy volunteers were randomly selected from the Paris Brain Institute's database. METHODS: Each participant underwent the Cog-First assessment, which comprises seven subtests, in standardized conditions. Ninety-five participants performed the alternative version one month later to assess the test-retest effect. The effects of gender, age, years of education and test version, as well as their two-way interactions, were evaluated by generalized linear models (GLMs). Formulas from the GLMS were extracted to calculate a corrected score that removes the effects of age, sex, version and years of education. This enables us to derive percentiles in a population of healthy volunteers, allowing the development of the standardization process. RESULTS: The results revealed a significant influence of gender, age, level of education and version on several sub-scores. Based on these results, the standardization process was implemented by calculating the percentiles on the corrected scores in the population of healthy volunteers. Test-retest analyses indicated a learning effect on four out of seven subtests. CONCLUSIONS: The standardization of Cog-First resulted in the development of score formulas adjusted for gender, age, education and version, integrated within the software for automated scoring. CLINICAL REHABILITATION IMPACT: This study establishes reliable norms for Cog-First, enabling meaningful score interpretation and clinical use, thereby facilitating early detection of cognitive impairments and potentially improving patient outcomes. Further research is necessary to determine the tool's applicability and sensitivity in brain-injured patients.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".