Concerns Regarding Article: 10.4103/ni.ni_800_22: Normative Data of Montreal Cognitive Assessment (MoCA) in Tamil Speaking Adults
Bibliographic record
Abstract
Sir, I am writing to express my concerns regarding the recently published article, “Normative Data of Montreal Cognitive Assessment (MoCA) in Tamil-Speaking Adults”[1] which presents normative data for the MoCA-TAM. There are established guidelines[2] for the translation and adaptation of psychological tests, including the development of population derived normative data. These guidelines will apply to cognitive screening tools as well. The neuropsychological interpretation and applications of these guidelines are also well-prescribed.[3] My concerns stem from several critical points that appear to compromise the validity and applicability of the presented findings. Firstly, it is well-established that the MoCA is validated for individuals aged 55 to 85 years, as stated on the MoCA test website and supported by existing normative data for Indian Malayalam speakers, as demonstrated in the work by Iype et al.[4] in this same journal. The current article, however, does not adequately address this age limitation and disregards the same in the methodology and data analysis. Secondly, the article lacks a comprehensive description of the MoCA-TAM test, including its psychometric properties. Without documented reliability, sensitivity, and specificity, along with a clear demonstration of its suitability for the target population, the normative data presented becomes questionable. There is a mention of a T-MoCA in the literature in the International Journal of Gerontology published by the Taiwan Society of Geriatric Emergency and Critical Care Medicine,[5] which appears to be distinct from the MoCA-TAM and also focused on elderly populations, further complicates the issue. Furthermore, the MoCA-TAM test is not available on the official website of the copyright holder and publisher, raising questions about its accessibility and legitimacy. Is this instead the MoCA 7 Tamil version or MoCA 8.1 or 8.2 or 8.3 Tamil versions which the authors have erroneously called as MoCA-TAM? Thirdly, the article acknowledges that several aspects of this screening tool were found to be unsuitable for the target population. I am concerned that these issues were not addressed before attempting to establish normative data. This oversight potentially invalidates the findings. Finally, the article provides only the number of participants found to be below the prescribed cutoff, without detailing their characteristics. This omission raises concerns about the potential misdiagnosis of younger individuals with mild cognitive impairment or cognitive impairment, simply due to the test’s inherent limitations or inadequate inclusion/exclusion criteria. These concerns lead me to question the overall validity and applicability of the normative data presented in the article. It will be helpful these issues are clarified or further investigation into the methods and conclusions of this study is done. Thank you for your attention to this matter. Sincerely, Porrselvi A.P., PhD 10/04/2025 Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.010 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.023 |
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".