P3‐256: VALIDATION OF THE INECO FRONTAL SCREENING IN A COLOMBIAN POPULATION
Bibliographic record
Abstract
The INECO Frontal Screening (IFS) was designed as an efficient evaluation of the executive function. IFS has shown to sensitively differentiate healthy from dementia population. Although originally created in Spanish, it is necessary to validate its use in a new Spanish population, since recent studies have shown clear differences in the daily use of the same language in different settings. Our objective was to validate the IFS in Colombian healthy, mild cognitive impairment (MCI) and Alzheimer disease (AD) populations. Patients with AD and MCI that were included and evaluated in our Memory Group between 2011 and 2013. Healthy population was recruited from community action groups. Subjects were evaluated with a standard protocol and a pre-defined diagnostic battery, MoCA, and IFS test. Final diagnosis was obtained by consensus. Nonparametric test were used and results expressed as medians. Convergent validity was established by Spearman's correlation with global deterioration scale (GDS), and MoCA, construct validity with Kruskall-Wallis and Wilcoxon tests. We used the Cronbach alpha to assess the internal consistency, and the ROC curve method to test the diagnostic accuracy and the optimal cutoff score. A total of 395 evaluations were done (139 healthy participants, 129 MCI and 127 AD). Mean age was 67.1±12.6 years, and 285 (72.2%) were female. Rho for convergence validity was -0.809 compared with GDS, and of 0.760 with MoCA. In the group's comparisons, IFS median was 22 [21-25] for healthy, 15 [14-17] in MCI and 12 [8-15] for AD, with p 0.001 for all comparisons. The Cronbach alpha was 0.886, and the ROC analysis revealed an AUC of 0.948 with an optimal cutoff of 17.5 with a sensitivity of 92.8% and specificity of 86.3. We validate the IFI for the Colombian population with MCI and AD, and established the optimal cutoff point. Our cutoff point is different from Chilean and Argentina validations. This findings underlie the differences between countries with the same linguistic root but regional differences in its use and the rational for a transcultural validation in each 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".