Supplementary Material for: The Single-Matrix Digit Cancellation Test (SMDCT), a screener for selective attention deficits: standardization in an Italian population sample and clinical usability in acute stroke patients
Bibliographic record
Abstract
Introduction. This study aimed at validating and providing Italian norms for the Single-Matrix Digit Cancellation Test (SMDCT), a cancellation task to screen for selective attention deficits, as well as providing clinical usability evidence for it in acute stroke patients. Methods. The SMDCT stimulus is a specular, 4-quadrant, horizontally oriented matrix, across which target distribution is homogeneous. Both Accuracy (-A) and Time (-T) outcomes were computed. N=263 healthy participants (HPs) and N=76 acute stroke patients were recruited. N=108 HPs also underwent the Mini-Mental State Examination, Frontal Assessment Battery (FAB) and Trail-Making Test (TMT), whilst patients were further assessed via the Mental Performance in Acute Stroke (MEPS). Regression-based norms were derived (Equivalent Scores). Construct and factorial validity, as well as case-control discrimination, were tested. Results. The matrix was underpinned by a two-component structure reflecting left and right hits. The SMDCT-T and -A were associated with TMT and FAB scores, respectively. Education predicted the SMDCT-A/-T, whereas age the SMDCT-T only. In patients, the SMDCT converged with the MEPS, also accurately discriminating them from HPs. An index of right-left difference differentiated right- from left-damaged patients. Conclusions. The SMDCT is a valid and normed screener for selective attention deficits, encompassing measures of both accuracy and time, whose adoption is encouraged in acute stroke patients. Relatedly, the horizontal disposition of its matrix does allow for the qualitative report of either leftwards of rightwards biases due to underlying visual or attentional-representational deficits in 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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.722 | 0.183 |
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".