SCIg vs. IVIg: let’s give patients the choice!
Bibliographic record
Abstract
We retrospectively analyzed 143 patients with primary immunodeficiency, followed in a single center, which were offered the choice of IVIg or SCIg. We analyzed the route more frequently chosen, and the consequences on compliance. In a first cohort (n = 51, average follow up 52 months), patients already on IVIg were offered the choice to stay on IVIg or to switch to SCIg (switch cohort). In a second cohort (n = 92, average follow up 11 months), newly diagnosed patients were offered the choice between IVIg and SCIg before the first injection (new cohort). In the switch cohort, 50/51 patients chose to switch to SCIg. Of these, 90% remained on SCIg. In the new cohort, 44/92 patients chose SCIg, of which 95% remained on SCIg. Among the 48 patients who chose IVIg, 73% switched to SCIg. Compliance issues were observed in only 10 patients. Giving patients the choice of treatment modality is a safe strategy in terms of compliance. Home-based SCIg is much more frequently chosen than hospital-based IVIg. Given the equal efficacy and safety between hospital-based IVIg and home-based SCIg, we believe that all patients should be given the choice regardless of physician’s belief of “idealness” of the candidate.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".