Strategies for managing paediatric patients in immunoglobulin clinical trials
Bibliographic record
Abstract
The continued demand for immunoglobulin treatment for multiple indications has placed considerable strain on the supply of immunoglobulin product. Reliance on a few manufacturers can significantly impact the availability of product. In addition, patient tolerability may vary from one product to another necessitating a choice of products to find the best treatment for an individual patient. Therefore, it is important to conduct clinical trials with new immunoglobulin products to ensure that there is adequate supply and choice of products available on the market. This is particularly important for immunodeficient patients who require treatment with immunoglobulins for life. A requirement for licensing by the Federal Food and Drug Administration and Health Canada is that every immunoglobulin licensing study includes some paediatric patients. Enrolling paediatric subjects in immunoglobulin clinical trials can be challenging due to the need for both consent and assent for enrolment, as well as the increased demands that the study protocol places on the child and family over their usual clinical care. Therefore, it is necessary to utilize strategies that make the demands of the protocol more tolerable for children, and to ensure that the study documentation reflects the unique needs of paediatric patients (Denhoff et al. 2015). Statement of novelty: This paper discusses strategies to facilitate enrolment and adherence to immunoglobulin study protocols that are unique to paediatric patients.
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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 | 0.184 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".