MétaCan
Menu
Back to cohort
Record W4407757530 · doi:10.2147/jmdh.s522605

The Impact of Online Interactive Platform Services on Oral Health Behaviors in Older adults with Mild Cognitive Impairment: Protocol for a Randomized Controlled Trial [Letter]

2025· article· en· W4407757530 on OpenAlexaboutno aff
Swarup Ghosh, K. Mondal, Manu S. Goyal

Bibliographic record

VenueJournal of Multidisciplinary Healthcare · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)Randomized controlled trialCognitive impairmentCognitionOral healthMedicineGerontologyComputer scienceAlternative medicineFamily medicinePsychiatryPathology

Abstract

fetched live from OpenAlex

The Impact of Online Interactive Platform Services on Oral Health Behaviors in Older Adults With Mild Cognitive Impairment: Protocol for a Randomized Controlled Trial", published in your journal, to be of considerable interest. 1The authors are to be commended for developing a study protocol centered around a novel online interactive platform and its potential to influence oral health behaviors in older adults experiencing mild cognitive impairment.Nevertheless, we believe some aspects of the study design require further elucidation.First, while the authors outlined the study's aims, they did not explicitly state the study's hypotheses.Assuming a twotailed hypothesis, the alternative hypothesis should be: "Online interactive platform services may have an effect on oral health behaviors in older adults with mild cognitive impairment".The corresponding null hypothesis would then be: "Online interactive platform services may have no effect on oral health behaviors in older adults with mild cognitive impairment".Second, including secondary outcomes such as cognitive level, perceived stress, and social support in the title would have provided readers with a clearer and more comprehensive understanding of the study's scope.Third, the authors did not report baseline scores for each outcome measure, including the Oral Health Behavior Scale, Mini-Mental State Examination, Perceived Stress Scale-10, Interpersonal Support Evaluation List-12, and Geriatric Oral Health Assessment Index. 2 Providing these scores would have clarified the initial extent of cognitive and oral health impairment at the time of participant recruitment.For example, the authors should have specified an MMSE score of 18-24 in the inclusion criteria to clearly indicate that only individuals with mild cognitive impairment were being recruited. 3dditionally, using the Montreal Cognitive Assessment (MoCA) scale to assess cognitive impairment would have been a better choice, as it is a more reliable and valid tool, with an inter-rater reliability of 0.96 and a Cronbach's alpha of 0.79. 4 Fourth, in the exclusion criteria, the authors could have considered excluding older adults with vision impairment, as it is commonly associated with cognitive decline and increased burden, making it difficult for participants to complete the online assessments.Fifth, the study's estimated sample size does not align with the actual calculation performed using G*Power software, 5 considering the specified effect size, statistical power, and alpha value, as shown in Figure 1.We encourage the authors to consider these points and believe that addressing these remarks and concerns will enhance the study protocol, allowing for more effective implementation in the main study.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.501
Teacher spread0.462 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Multidisciplinary HealthcareSame topicMobile Health and mHealth ApplicationsFrench-language works237,207