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Record W3046982435 · doi:10.1111/head.13918

Access to Migraine Treatments in Ontario, Canada: A Review of the Ontario Drug Benefit Program

2020· review· en· W3046982435 on OpenAlexaffabout
Susan Cape

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2020
Typereview
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMigraineDrugMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: This paper provides a critical review of the decision-making process of the Ontario Ministry of Health and Long-Term Care (MOHLTC) regarding which migraine treatment drugs will be covered under the Ontario Drug Benefit Formulary (ODB). MAIN TEXT: Under MOHLTC policy, triptans and OnabotulinumtoxinA are available to patients only through the Exceptional Access Program (EAP). This policy, and justifications for it, are examined with reference to clinical guidelines, patient experiences, and health policy literature. The contexts and consequences of compromised access are outlined. Improvements in access to these treatments are suggested by highlighting how a country with similar healthcare infrastructure - Australia - employs policies that more adequately meet the needs of migraine patients as they secure treatments. CONCLUSIONS: Despite clinically significant gains in the discovery of safe and effective migraine-specific treatments the ODB thus far has failed to align its practice with current clinical recommendations. This forces patients to rely heavily on medication that, while still effective for some, is potentially suboptimal. This review concludes it is prudent, at minimum, to follow clinical recommendations that advocate for the removal of triptans from EAP and recategorize them as Limited Use drugs. Ideally, moving them to a general benefit would further remove the barriers experienced by patients attempting to access this treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.021
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.065
GPT teacher head0.352
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2020
Admission routes2
Has abstractyes

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