2967 Utilisation of high efficacy therapy for managing multiple sclerosis in Australia
Bibliographic record
Abstract
Background Ofatumumab is a self-administered disease modifying therapy (DMT) approved in Australia for relapsing multiple sclerosis (RMS). We have previously described a novel, high efficacy therapy (HET) classification system 1 which includes ofatumumab and other monoclonal antibody therapies, ocrelizumab, natalizumab and alemtuzumab. Given the reimbursement of ofatumumab in October 2021, and in the context of this proposed classification system, we analysed the utilisation of these HETs for multiple sclerosis (MS) in Australia.Methods DMT utilisation was analysed using an unbiased 10% sample of the Pharmaceutical Benefits Scheme Claims database as supplied by Services Australia. Patients-on-therapy (PoT) counts were attributed to the most recently prescribed DMT whilst initiations were defined as the first time a patient was prescribed a new DMT (treatment-naïve and switch) during a specified time-period.Results From September, 2021 (pre-ofatumumab reimbursement) to October, 2023, PoT for any DMT rose by 10.4% (23,070-to-25,460). In the same period, HET PoT rose by 31.3% (9,830-to-12,910), driven by increases in ofatumumab (+2,010), ocrelizumab (+1,050), and natalizumab (+110). By contrast, non-HET PoT fell by 5.2% (13,240-to-12,550), driven by fingolimod (-1,020) and dimethyl fumarate (-530). In the 12 months prior to ofatumumab reimbursement, 46.7% (2,200/4,710) of DMT initiations were HET but this proportion rose to 53.1% (2,920/5,500) in the most recent 12 month period (November-October 2023), driven by ofatumumab (1,200; 21.8%), ocrelizumab (1,090; 19.8%), and natalizumab (730; 11.5%).Conclusions The availability of a self-administered, at-home HET option in ofatumumab has correlated with an expansion of HET utilisation in Australia.Reference Butzkueven, et al. P755. ECTRIMS-ACTRIMS 2023.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".