Idiopathic Inflammatory Myopathies and Malignancy Screening: A Survey of Current Practices Amongst Canadian Neurologists and Rheumatologists
Bibliographic record
Abstract
INTRODUCTION/AIMS: Although the need for malignancy screening in idiopathic inflammatory myopathies (IIM) is generally accepted, data to guide the choice and timing of investigations are limited. Our aim was to characterize the gaps and uncertainties amongst Canadian neurologists and rheumatologists with respect to malignancy screening in IIM. METHODS: An online survey consisting of 18 multiple-choice questions related to malignancy screening practices was distributed to adult neurologists and rheumatologists practising in Canada, and survey responses were described and compared between groups. RESULTS: Of 69 participants, the majority (95.7%) performed malignancy screening. However, there was variability in practice including delegation and choice of screening tests, influence of patient-specific factors, and timing of repeat testing relative to original testing. Only 18.2% of respondents were confident in their malignancy screening practices. The most significant perceived knowledge gap was lack of consensus or guidelines on choice and frequency of malignancy screening (92.8%). Compared with neurologists, rheumatologists saw a higher number of IIM patients per year (72.5% vs. 41.4% reported five or more, p = 0.009), were more likely to consider patient risk factors and order more investigations, while neurologists were more likely to repeat testing. DISCUSSION: Variability and knowledge gaps exist amongst neurologists and rheumatologists with regard to malignancy screening in IIM patients. The identified variability and lack of confidence may lead to lack of standardization of care, and potentially either under- or over-investigating of IIM patients for malignancy. Further research is required to better understand the optimal choice of tests and timing of repeat investigations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".