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Record W4220790661 · doi:10.1111/head.14284

Effects of acute and preventive therapies for episodic and chronic cluster headache: A scoping review of the literature

2022· review· en· W4220790661 on OpenAlexaff
Ioana Medrea, Suzanne Christie, Stewart J. Tepper, Kednapa Thavorn, Brian Hutton

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2022
Typereview
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsOttawa HospitalCommunications Security EstablishmentUniversity of Ottawa
Fundersnot available
KeywordsMedicineObservational studyCluster headacheRandomized controlled trialPlaceboMEDLINEPsychological interventionClinical trialPhysical therapySystematic reviewPopulationIntensive care medicineAlternative medicineInternal medicinePsychiatryMigrainePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Cluster headache is the most common primary headache disorder of the trigeminal autonomic cephalalgias, and it is highly disabling. OBJECTIVE: We undertake a scoping review to characterize therapies to prevent and acutely treat cluster headache, characterize trial methodology utilized in studies, and recommend future trial "good practices." We also assess homogeneity of studies and feasibility for future network meta-analyses (NMAs) to compare acute and preventive treatments for cluster headache. METHODS: A priori protocol for this scoping review was registered and available on Open Science Forum. We sought studies that enrolled adult patients with cluster headache as identified by accepted diagnostic criteria. Both randomized controlled trials (RCTs) and observational studies (with a control group) were included. The interventions of interest were medications, procedures, devices, surgeries, and behavioral/psychological interventions, whereas comparators of interest were placebo, sham, or other active treatments. Outcomes were predefined; however, we did not exclude studies lacking these outcomes. A systemic search was conducted in Ovid Medline, Embase, and Cochrane. We performed a targeted search for conference abstracts from journals prominent in the field. RESULTS: We identified 56 studies: 45 RCTs, four studies only available in clinical trial registries, and seven observational studies. Of the 45 RCTs, 20 focused on acute therapies and 25 on preventive therapies. Overall, we determined that it is feasible to pursue a NMA for acute therapy focusing on 15 or 30-min headache reduction for acute trials, as we identified 11 trials in the combined population of patients with either episodic or chronic cluster headache (2 trials in populations with chronic cluster headache were also found). For preventive therapy of cluster headache, we identified trials with common outcomes that may be considered for NMA, however, as these trials had differences in treatment effect modifiers that could not be corrected, NMAs appear infeasible for this indication. We identified new studies looking at noninvasive vagal nerve stimulation, sphenopalatine ganglion stimulation, prednisone, and oxygen published since the most recent systematic review in the field, although these acute treatments were previously identified as effective. However, for calcitonin gene-related peptide (CGRP) monoclonal antibodies, galcanezumab demonstrated effectiveness in episodic cluster headache, but a lack of effectiveness in chronic cluster headache, and fremanezumab was not effective for episodic nor chronic cluster headache. This finding highlights that CGRP monoclonal antibodies may not show a class effect in cluster headache prevention and need to be considered individually. CONCLUSIONS: We describe the treatment landscape of cluster headache for both acute and preventive treatments. Last, we present the NMAs we will undertake in acute therapies of cluster headache.

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)0.028
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.091
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0270.019
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.356
Teacher spread0.329 · 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 designSystematic review
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

Citations9
Published2022
Admission routes1
Has abstractyes

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