P.031 Access to immunoglobulin treatment for CIDP patients during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Immunoglobulin supplies are limited; we aimed to determine if the COVID-19 pandemic was associated with difficulty accessing immunoglobulin treatment for patients diagnosed with Chronic Inflammatory Demyelinating Polyneuropathy (CIDP). Methods: A retrospective cross-sectional study was conducted with CIDP patients (n=16, 68.75% female, mean age 60.38 ± 11.32) recruited from three Montreal tertiary care institutions. Patients completed a questionnaire inquiring about changes in their immunoglobulin treatment during the pandemic and about their quality of life. We used weighted chi-squared statistical tests and Cramer’s V correlation ratios to measure associations with treatment change. Results: Eighteen months after the pandemic started, 25% of our population were receiving immunoglobulin treatment at a different frequency, 6.3% were receiving a different dose, 12.5% were receiving a different dose and frequency, and 6.3% were receiving a different treatment. Reasons associated with treatment change were worsening of neurological condition (18.8%; Cramer’s V=0.480; p-value=0.055), improvement of neurological condition (25%; Cramer’s V=0.577; p-value=0.021) and reduced availability of treatment (6.3%; Cramer’s V=0.258; p-value=0.302). There were no significant correlations between lower quality of life (p-value=0.323) or lower Rasch-built Overall Disability Scale score (p-value=0.574) and treatment change. Conclusions: Difficulty accessing immunoglobulin treatment was not significantly associated with treatment change for CIDP patients during the COVID-19 pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".