Correction to: Cystic fibrosis–related diabetes onset can be predicted using biomarkers measured at birth
Bibliographic record
Abstract
The original article can be found online at https://doi.org/10.1038/s41436-020-01073-x. Correction to: Genetics in Medicine23: 927–933; https://doi.org/10.1038/s41436-020-01073-x; Article published online 26 January 2021 The risk alleles of rs1964986 (PRSS1) and rs959173 (CAV1) should be the C allele for both variants instead of the A and T alleles listed in the paper. The changes have been reflected in both Figure 1 and Table 2 shown below.Table 2Effect sizes (hazard ratios) and the 95% confidence intervals (CIs) fitted using a multivariate Cox PH model in the CGS. Risk allele/risk group noted in parentheses after the listed predictor.Gene annotationPredictorHazard ratio95% CICFTRCFTR mutation score3.02(2.01, 4.54)—Sex (female)1.48(1.26, 1.74)SLC5A8rs12318809 (G)1.35(1.16, 1.57)CAV1rs959173 (C)1.27(1.10, 1.47)PRSS1rs1964986 (C)1.23(1.09, 1.38)SLC26A9rs4077468 (A)1.20(1.07, 1.34)NRG1rs7822917 (T)1.31(1.16, 1.48)—Meconium ileus (MI)1.29(1.05, 1.59)TCF7L2rs7903146 (T)1.18(1.05, 1.34) Open table in a new tab The original article has been corrected. Cystic fibrosis–related diabetes onset can be predicted using biomarkers measured at birthGenetics in MedicineVol. 23Issue 5PreviewCystic fibrosis (CF), caused by pathogenic variants in the CF transmembrane conductance regulator (CFTR), affects multiple organs including the exocrine pancreas, which is a causal contributor to cystic fibrosis–related diabetes (CFRD). Untreated CFRD causes increased CF-related mortality whereas early detection can improve outcomes. Full-Text PDF Open Access
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.090 | 0.043 |
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".