P.038 Idiopathic inflammatory myopathies and malignancy screening: a survey of the current practices amongst Canadian neurologists and rheumatologists
Bibliographic record
Abstract
Background: There is a well-established association between idiopathic inflammatory myopathies (IIM) and malignancy. There are no evidence-based guidelines amongst neurologists and rheumatologists on the choice and timing of malignancy investigations. Our aim is to characterize the current gaps and uncertainties amongst neurologists and rheumatologists with malignancy screening in IIM patients. Methods: An online survey consisting of 18 multiple-choice questions related to IIM malignancy screening was distributed to adult neurologists and rheumatologists in Canada. Quantitative and descriptive analysis was performed. Results: The majority of respondents (96%, n=68) performed malignancy screening. There was variability in practice including delegation and choice of screening tests, influence of patient-specific factors, and time and length of repeat testing. Only 18% of respondents were confident in their malignancy screening practices. Between neurologists and rheumatologists, there were differences in the number of IIM patients seen, consideration of patient-specific factors and choice of screening investigations. Further details and data will be presented at the conference. Conclusions: There is a lack of consensus and confidence in the choice and timing of malignancy investigations in IIM, with neurologists and rheumatologists differing in their approaches. Further research is required to better understand the relationship between IIM and malignancy to create expert-led consensus guidelines.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".