1ISG-003 Study of the use of intravenous immunoglobulins during the fourth quarter of 2018 and analysis of its off-label use
Bibliographic record
Abstract
Background and importance The use of intravenous immunoglobulins (IVIg) has increased as a result of their therapeutic usefulness in a great number of diseases. Despite this, IVIg label indications remain limited, so it is interesting to study their off-label use. Aim and objectives To describe the use of IVIg in our hospital for 3 months and to determine if they have been used for labelled indications. Material and methods This was a retrospective study (October–December 2018) and a descriptive analysis of the use of IVIg per patient and clinical indication. Information was collected from the hospital’s information systems and the computer records of the Farmatools software. Results Eighty-nine patients received IVIG during the study period, with an average age of 61 years at the end of the study (3 months–86.7 years); there were 40 (45%) men and 49 (55%) women. When IVIg were used as replacement therapy, the dosage used was 200–400 mg/kg every 3–5 weeks. In the remaining indications, the dose used per treatment cycle was 1–2 g/kg divided over 2–5 days. IVIG were used for labelled indications in 80% of patients (71/89) compared with 20% for off-label indications (18/89). Among the latter, the indications were: demyelinating neuropathies (6/18), myasthenia gravis (2/18), myopathies (2/18), encephalitis/encephalomyelitis (2/18), Morvan syndrome (1/18), syndrome paraneoplastic (1/18), refractory atopic dermatitis (1/18), paraneoplastic dermatomyositis (1/18), scleroderma (1/28) and antisynthetase syndrome (1/18). Conclusion and relevance The use of IVIG in unauthorised indications was frequent (20%), mainly in the field of neurology. This justifies the development of a protocol for the use of IVIG in this field for those indications with more scientific evidence and more common use: demyelinating neuropathies, myasthenia gravis and myopathies. References and/or acknowledgements No conflict of interest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".