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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".