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Record W3008545807

Laser Interstitial Thermal Therapy for Epilepsy and/or Brain Tumours: A Review of Clinical Effectiveness and Cost-Effectiveness [Internet]

2019· review· en· W3008545807 on OpenAlexaboutno aff
Dinsie Williams, Hannah Loshak

Bibliographic record

Venuenot available
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicChemical Reactions and Isotopes
Canadian institutionsnot available
Fundersnot available
KeywordsEpilepsyMedicineEpilepsy surgeryNeurocognitiveCraniotomySurgeryPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Epilepsy is a chronic neurological condition that is characterized by spontaneous seizures that can result in mild symptoms such as a lapse in concentration or may be serious enough to cause unconsciousness or premature death. Between 2010 and 2012, an estimated 139,200 Canadians suffered from epilepsy.Epilepsy has a diverse etiology ranging from genetic pre-disposition to tumours, and brings a level of complexity to the diagnosis and treatment of the condition. Incidentally, epileptic seizures are associated with epileptogenic zones in the brain which have been the target of treatment options.The first line of treatment for epilepsy involves the use of anticonvulsant drug therapy, however, one third of patients are unable to experience complete control of their seizures following the administration of two or more pharmaceuticals. For patients with such drug-resistant epilepsy, the primary treatment approach is open surgery (e.g., craniotomy, temporal lobectomy) which seeks to provide relief from seizures by destroying epileptogenic zones or detaching them from other parts of the brain. Fear of possible treatment-related complications such as post-operative neurocognitive decline has inhibited the wide-spread acceptance of open intracranial surgery and prompted interest in alternative techniques.Laser interstitial thermal therapy (LITT) or stereotactic laser ablation (SLA) is a minimally invasive technique that offers an alternative approach to open surgery for eliminating epileptogenic zones, deep-seated intracranial tumours, and recurrent metastases. LITT involves using high-intensity laser light to induce thermocoagulative necrosis (i.e., destruction of tissue). The laser light is produced by a probe which is made out of an optical fiber tube or flexible catheter with a light-diffusing tip., The probe is stereotactically placed over the volume of tissue that is targeted for ablation through a hole that is drilled into the skull., The energy from the laser light is converted to heat within the target volume, inducing a cascade of enzymes that leads to protein denaturation, membrane dissolution, and vessel sclerosis, all precursors of necrosis. Since the emergence of intercranial LITT in the 1980s, technical advancements have been made that include the development of cooling systems to control the heat profile of the tip of the laser probe and the use of thermal magnetic resonance (MR) in MR-guided LITT to localize subcentimeter epileptic zones and minimize the target area for laser ablation in real-time. Health Canada has licensed two systems for laser ablation., They are the NeuroBlate System and the Visualase MRI-Guided Thermal Ablation System.A rapid review of the clinical effectiveness and cost-effectiveness of LITT over any comparator for intracranial lesions and epilepsy published by CADTH in 2015 reported that the quantity and quality of the available evidence on clinical efficacy was limited and that no cost-effectiveness studies were identified. This current review aims to summarize updated evidence regarding the clinical effectiveness, safety, and cost-effectiveness of LITT for the treatment of epilepsy and brain tumours.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.359
GPT teacher head0.581
Teacher spread0.222 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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