Corrigendum: EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome
Bibliographic record
Abstract
Corrigendum on: Proteau-Lemieux M, Knoth IS, Agbogba K, Côté V, Barlahan Biag HM, Thurman AJ, Martin CO, Bélanger AM, Rosenfelt C, Tassone F, Abbeduto LJ, Jacquemont S, Hagerman R, Bolduc F, Hessl D, Schneider A, Lippé S. EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome. Front Psychiatry. 2021 Nov 11;12:716707. doi: 10.3389/fpsyt.2021.716707. PMID: 34858220; PMCID: PMC8632368.In the original article, there was an error. The abstract states that we compared 26 FXS participants with 7 neurotypical controls. This is incorrect. As correctly stated in the methods and result sections, we compared 26 FXS participants to 77 neurotypical controls.Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5-28 years), and 77 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site.
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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 | 0.003 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 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".