Erratum: microRNA dysregulation in multiple sclerosis
Bibliographic record
Abstract
To the Editor,In our review article entitled “MicroRNA dysregulation in Multiple Sclerosis” published online on January 22nd, 2013, we have made an error that we would like to correct. We request to change the following sentence in the review article: "Examination of Treg cells in MS (De Santis et al., 2010) has identified differential expression of 23 miRNAs compared with healthy controls. Of particular interest, miR-106b and miR-25 were among the significantly decreased miRNAs, both of which modulate TGF-β signaling (Petrocca et al., 2008)." To "Examination of Treg cells in MS (De Santis et al., 2010) has identified differential expression of 23 miRNAs compared with healthy controls. Of particular interest, miR-106b and miR-25 were among the significantly increased miRNAs, both of which modulate TGF-β signaling (Petrocca et al., 2008)." We would also like to apply this change in a revised figure that reflects the correct directionality of the altered miRNA expression in Treg cells in MS.We apologize to the readers of Frontiers in Genetics and to the authors of the De Santis et al., manuscript, for this error that was not corrected during the review process of our manuscript.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".