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Record W2155108920 · doi:10.3747/co.v13i6.107

The Use of Prophylactic Anticonvulsants in Patients with Brain Tumours—A Systematic Review

2006· article· en· W2155108920 on OpenAlexaffvenueabout
James Perry, Lorne Zinman, Ann F. Chambers, Karen Spithoff, N Lloyd, Normand Laperrière

Bibliographic record

VenueCurrent Oncology · 2006
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsPrincess Margaret Cancer CentreMcMaster UniversityCancer Care OntarioSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineSystematic reviewMEDLINERandomized controlled trialGuidelineCochrane LibraryAdverse effectIntensive care medicineMeta-analysisEvidence-based medicineAlternative medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

QUESTIONS: Should patients with newly diagnosed brain tumours receive prophylactic anticonvulsants to reduce seizure risk? What is the best practice for patients with brain tumours who are taking anticonvulsant medications but who have never had a seizure? PERSPECTIVES: Patients with primary or metastatic brain tumours who have never had a seizure still have a 20% risk of experiencing a seizure over the course of their disease. Because considerable practice variation exists in regard to the management of patients with brain tumours who have never had a seizure, and because conflicting evidence has been reported, the Neuro-oncology Disease Site Group (dsg) of Cancer Care Ontario's Program in Evidence-based Care felt that a systematic review of the evidence was warranted. OUTCOMES: Outcomes of interest were incidence of seizures and adverse effects of prophylactic anticonvulsant therapy. METHODOLOGY: The medline and Cochrane Library databases were systematically searched for relevant evidence. The review included fully published reports or abstracts of randomized controlled trials (rcts), systematic reviews, meta-analyses, and practice guidelines. The present systematic review was reviewed and approved by the Neuro-oncology dsg, which comprises medical and radiation oncologists, surgeons, neurologists, a nurse, and a patient representative. QUALITY OF EVIDENCE: The literature search located one evidence-based practice guideline, one systematic review, and five rcts that addressed prophylactic anticonvulsants for patients with brain tumours. Evidence for the best management of seizure-naïve patients who are already taking anticonvulsants was limited to one retrospective study and exploratory analyses within several rcts. BENEFITS AND HARMS: Pooled results of the five rcts suggest that the incidence of seizures in patients who receive prophylactic anticonvulsants is not significantly different from that in patients who do not receive anticonvulsants (relative risk: 1.04; 95% confidence interval: 0.70 to 1.54; p = 0.84). This analysis accords with results from a published meta-analysis. Evidence is insufficient to determine whether patients who are currently taking anticonvulsants but who have never had a seizure should taper the anticonvulsants. Patients who received anticonvulsants reported adverse effects, including rash, nausea, and hypotension, but whether these effects are a result of the anticonvulsants or of other treatments could not be determined. CONCLUSIONS: Based on the available evidence, the routine use of postoperative anticonvulsants is not recommended in seizure-naïve patients with newly diagnosed primary or secondary brain tumours, especially in light of a significant risk of serious adverse effects and problematic drug interactions. Because data are insufficient to recommend whether anticonvulsants should be tapered in patients who are already taking anticonvulsants but who have never had a seizure, treatment must be individualized.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.387
Teacher spread0.301 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations47
Published2006
Admission routes3
Has abstractyes

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