Convergence and equating norms between the Telephone Interview for Cognitive Status (TICS), the MMSE and the MoCA in an Italian population sample
Bibliographic record
Abstract
This study aimed at testing the convergence and deriving equating norms between the Telephone Interview for Cognitive Status (TICS) and the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in an Italian population sample. Four-hundred and eighty two healthy Italian native-speaker (300 females; age: 57.8 ± 15.5, range = 20–94; education: 13.1 ± 3.8, range = 5–25) underwent the TICS ( range = 1–41), MMSE and MoCA. An additional Delayed Recall of the 10-word list was administered as the last task of the TICS to compute a further total (TICS & DR; range = 1–51). Convergence between the TICS/TICS & DR and in-person screeners was tested via Bonferroni-corrected Spearman’s coefficients, whilst equating norms were derived via a Log-linear Smoothing Equipercentile Equating (LSEE) approach. A two one-sided test (TOST) procedure was run to test the equivalence between empirical and LSEE-derived scores. TICS scores converged with both MMSE ( r s =0.34; p <.001) and MoCA scores ( r s =0.42; p <.001)– the same being true for the TICS & DR (MMSE: r s =0.36; p <.001; MoCA: r s =0.42; p <.001). Cross-walks were estimated to derive TICS/TICS & DR scores from the MMSE/MoCA, and vice-versa. The algorithm could not compute the conversions for TICS, MMSE and MoCA scores < 22, <21 and < 14, respectively. TOST procedures revealed that all comparisons yielded equivalence except for those aimed at deriving TICS from MMSE scores and TICS & DR from both the MMSE and the MoCA. The Italian TICS validly captures examinees’ cognitive efficiency as measured by MMSE or MoCA; derived cross-walks between the TICS and MMSE/MoCA allows for a flexible use of in-person and telephone-based screeners.
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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 | 0.021 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".