Anti-transcription intermediary factor 1-gamma IgG2 isotype is associated with cancer in adult dermatomyositis: an ENMC multinational study
Bibliographic record
Abstract
OBJECTIVE: To assess the role of the anti-TIF1γ auto-antibody (aAb) IgG2 isotype as a biomarker of cancer in anti-TIF1γ aAb-positive adult DM. METHODS: International multicentre retrospective study with the following inclusion criteria: (i) diagnosis of DM according to ENMC criteria; (ii) presence of anti-TIF1γ IgG aAb determined using an in-house addressable laser bead immunoassay (ALBIA) from cryopreserved serums sampled at time of DM diagnosis and (iii) available baseline characteristics and follow-up data until the occurrence of cancer and/or a minimum follow-up of 1 year for patients without known cancer at diagnosis. Detection and quantification of anti-TIF1γ IgG2 aAb was done using the in-house ALBIA. In addition, a recent ELISA commercial kit was used for anti-TIF1γ IgG aAb quantification. RESULTS: A total of 132 patients (mean age 55±15 years) of whom 72 (54.5%) had an associated cancer were analysed. The association between the presence of cancer and the presence of anti-TIF1γ IgG2 aAb was statistically significant (P = 0.026), with an OR of 2.26 (95% CI: 1.10, 4.76). Patients with cancer displayed significantly higher anti-TIF1γ IgG2 aAb ALBIA values with a median value of 1.15 AU/ml (IQR: 0.14-9.76) compared with 0.50 AU/ml (IQR: 0.14-1.46) for patients without cancer (P = 0.042). In addition, patients with cancer displayed significantly higher anti-TIF1γ IgG aAb ELISA values with a median value of 127.5 AU/ml (IQR: 81.5-139.6) compared with 93.0 AU/ml (IQR: 54.0-132.9) for patients without cancer (P = 0.004). CONCLUSION: These results suggest considering anti-TIF1γ IgG2 ALBIA and IgG ELISA values as biomarkers of cancer in anti-TIF1 γ aAb-positive adult DM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".