Noise and Cognitive Performance in Developing Brain using Functional MRI: A scoping review protocol
Bibliographic record
Abstract
This protocol aims to guide the process to conduct a scoping review on noise and cognitive performance in developing the brain using functional MRI. This review benefits future research by providing a clear mapping of evidence. This protocol adhered to Arksey and O'Malley's scoping review methodological framework. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews (PRISMA-ScR) was implemented to report the full scoping review. This protocol facilitated a well-structured mapping of evidence. The findings from scoping review will be made public through conferences and journal publications. Keywords: Scoping review protocol; Adolescents’ cognitive performance; Noise; functional MRI. eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i19.3256
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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 | 0.156 | 0.142 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.100 | 0.028 |
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".