Bibliographic record
Abstract
Multiple Sclerosis is the most common non-traumatic cause of neurological disability in \nyoung people. Until recently few treatments existed for Multiple Sclerosis and Interferon \nbeta was the first and remains the most commonly prescribed. As a biological product it \ninduces antibodies to the protein which may abrogate the efficacy of the drug (neutralising \nantibodies - NAbs). Testing for these antibodies has been problematic as biological assays \nare difficult to standardise, time consuming and expensive. In the experiments described \nhere we sought to develop a novel cell-based reporter-gene assay to reliably test for Nabs, \nto explore the relationship between NAbs, treatment efficacy, biological activity and \ncorrelate NAb titres with in vivo biomarker induction to establish guidelines to interpret \nresults. The work presented in this thesis describes the development and validation of the \nluciferase assay and has shown that subjects who develop NAbs experienced increased \nrelapse rates, (which lag behind the appearance of NAbs). Evaluating the in vivo biological \nresponse to IFNβ injection it was established that in subjects with NAbs there was titre \ndependent loss of bioactivity with reduced myxovirus resistance protein A (MxA) level in \nthose with titres 100 – 600 NU and absent response in those with titres > 600 NU. Opinion \namongst neurologists (UK, USA, Canada and Austria) regarding NAbs was evaluated and \nrevealed uncertainty of their significance in the clinical setting, and the reluctance of some \nneurologists to incorporate NAb testing into routine practice. Results were similar in the \ncountries surveyed in that 90 – 100% were aware of NAbs and thought they abrogate \nclinical efficacy, yet few routinely tested for them, particularly in the UK. The validated \nluciferase assay has since been dissemintated to 9 countries. Since it’s launch in the UK in \n2006 over 4,000 patients have been tested for NAbs. This work directly translates into \nclinical practice and provides a useful service to aid management of subjects with MS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".