Anticonvulsant and gabapentinoids pharmacotherapy in the multiple sclerosis prodrome: A population-based matched cohort study
Bibliographic record
Abstract
BACKGROUND: Before disease onset, multiple sclerosis (MS) persons fill more prescriptions than controls, including for pain. However, knowledge regarding neuropathic pain-related medications is lacking OBJECTIVE: Compare odds of anticonvulsant/gabapentinoid prescriptions for 4,862 MS-cases versus 22,669 controls, pre-MS onset (defined as first demyelinating disease-related event). METHODS: Matched-cohort study using administrative data (1996-2013), comparing the odds of anticonvulsant/gabapentinoid prescriptions pre-MS onset using multivariable logistic regression. RESULTS: Versus controls, MS-cases were more likely to fill prescriptions for anticonvulsants (aOR[adjusted odds ratios] = 3.1,95 % confidence interval[CI]:2.8,3.4), gabapentinoids (aOR = 4.1,95 %CI:3.6,4.6), and gabapentinoids without anticonvulsants (aOR = 3.9,95 %CI:3.4,4.5). CONCLUSION: MS-cases filled anticonvulsant and gabapentinoid prescriptions more than matched controls pre-MS onset.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".