Discontinuation of immunoglobulin replacement therapy in patients with secondary antibody deficiency
Bibliographic record
Abstract
INTRODUCTION: Secondary immunodeficiency is becoming a greater medical concern as the usage of immunosuppressive and biological treatments has increased. Individuals with certain medical conditions, such as hematological malignancies, can also have secondary immunodeficiency. Immunoglobulin replacement therapy (IGRT), which has been used for decades in inherited or primary immunodeficiency, provides some protection to patients with acquired and predominant antibody deficiency, i.e. secondary antibody deficiency (SAD). However, IGRT is costly, and supplies are limited. Although there are clinical guidelines on when to initiate IGRT, there is no guideline on when to discontinue it. AREAS COVERED: The authors reviewed existing literature and provided an overview of the current state of knowledge regarding IGRT discontinuation in SAD patients. EXPERT OPINION: Long-term supplementary immunoglobulin may not be necessary. Although it is possible to successfully transition away from IGRT in individuals with SAD, evidence-based practices are limited. Without clear guidelines and reliable prognostic markers, IGRT discontinuation practices are restricted to clinical judgment. For this reason, additional research should be conducted to identify markers that indicate the recovery of humoral immunity. Furthermore, the derivation and validation of a set of combined clinical and laboratory criteria to allow safe and timely IGRT discontinuation is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".