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Record W4385659022 · doi:10.1007/s40520-023-02509-5

Correction to: The Montreal Cognitive Assessment (MoCA): updated norms and psychometric insights into adaptive testing from healthy individuals in Northern Italy

2023· erratum· en· W4385659022 on OpenAlexaboutno aff
Edoardo Nicolò Aiello, Chiara Gramegna, Antonella Esposito, Valentina Gazzaniga, Stefano Zago, Teresa Difonzo, Ottavia Maddaluno, Ildebrando Appollonio, Nadia Bolognini

Bibliographic record

VenueAging Clinical and Experimental Research · 2023
Typeerratum
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentPsychologyCognitionComputerized adaptive testingPsychometric testingClinical psychologyCognitive impairmentPsychometricsGerontologyApplied psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

In the original version of this article, some typos regarding the adjustment coefficients for raw MoCA-Visuo-spatial (MoCA-VS) and MoCA-Attention (MoCA-A) scores (Table 3) were present and have now been amended (amended MoCA-VS cell: age=35, education=8; amended MoCA-A cells: age=55, education=8; age=35, education=13). Moreover, some imprecisions regarding the discussion of the discrepancies between the present and previous normative studies have been amended (page 379, Discussion: "More specifically, ESs allotments here reported proved to be stricter than those of Santangelo et al.’s [1] with regard to MoCA-total, -VS, -EF and -A, whereas less strict with regard to MoCA-O and Conti et al.’s [2] total"). We are thankful to the Researcher that has drawn our attention on such elements. The updated Table 3 is below: (Table presented.) Adjustment grids according to age and education for MoCA total and subtest raw scores Sub-test Education Age 35 40 45 50 55 60 65 70 75 80 85 90 95 Total 5 ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.510
GPT teacher head0.562
Teacher spread0.052 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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