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Record W2802309133 · doi:10.3171/2017.9.jns17545

Stereotactic radiosurgery for trigeminal neuralgia: a systematic review

2018· review· en· W2802309133 on OpenAlexaff
Constantin Tuleasca, Jean Régis, Arjun Sahgal, Antônio De Salles, Motohiro Hayashi, Lijun Ma, Roberto Martínez‐Álvarez, Ian Paddick, Samuel Ryu, Ben J. Slotman, Marc Levivier

Bibliographic record

VenueJournal of neurosurgery · 2018
Typereview
Languageen
FieldMedicine
TopicTrigeminal Neuralgia and Treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsRadiosurgeryMedicineGuidelineTrigeminal neuralgiaCyberknifeMEDLINENuclear medicineRadiologyMedical physicsSurgeryRadiation therapyPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: The aims of this systematic review are to provide an objective summary of the published literature specific to the treatment of classical trigeminal neuralgia with stereotactic radiosurgery (RS) and to develop consensus guideline recommendations for the use of RS, as endorsed by the International Society of Stereotactic Radiosurgery (ISRS). METHODS: The authors performed a systematic review of the English-language literature from 1951 up to December 2015 using the Embase, PubMed, and MEDLINE databases. The following MeSH terms were used in a title and abstract screening: "radiosurgery" AND "trigeminal." Of the 585 initial results obtained, the authors performed a full text screening of 185 studies and ultimately found 65 eligible studies. Guideline recommendations were based on level of evidence and level of consensus, the latter predefined as at least 85% agreement among the ISRS guideline committee members. RESULTS: The results for 65 studies (6461 patients) are reported: 45 Gamma Knife RS (GKS) studies (5687 patients [88%]), 11 linear accelerator (LINAC) RS studies (511 patients [8%]), and 9 CyberKnife RS (CKR) studies (263 patients [4%]). With the exception of one prospective study, all studies were retrospective.The mean maximal doses were 71.1-90.1 Gy (prescribed at the 100% isodose line) for GKS, 83.3 Gy for LINAC, and 64.3-80.5 Gy for CKR (the latter two prescribed at the 80% or 90% isodose lines, respectively). The ranges of maximal doses were as follows: 60-97 Gy for GKS, 50-90 Gy for LINAC, and 66-90 Gy for CKR.Actuarial initial freedom from pain (FFP) without medication ranged from 28.6% to 100% (mean 53.1%, median 52.1%) for GKS, from 17.3% to 76% (mean 49.3%, median 43.2%) for LINAC, and from 40% to 72% (mean 56.3%, median 58%) for CKR. Specific to hypesthesia, the crude rates (all Barrow Neurological Institute Pain Intensity Scale scores included) ranged from 0% to 68.8% (mean 21.7%, median 19%) for GKS, from 11.4% to 49.7% (mean 27.6%, median 28.5%) for LINAC, and from 11.8% to 51.2% (mean 29.1%, median 18.7%) for CKR. Other complications included dysesthesias, paresthesias, dry eye, deafferentation pain, and keratitis. Hypesthesia and paresthesia occurred as complications only when the anterior retrogasserian portion of the trigeminal nerve was targeted, whereas the other listed complications occurred when the root entry zone was targeted. Recurrence rates ranged from 0% to 52.2% (mean 24.6%, median 23%) for GKS, from 19% to 63% (mean 32.2%, median 29%) for LINAC, and from 15.8% to 33% (mean 25.8%, median 27.2%) for CKR. Two GKS series reported 30% and 45.3% of patients who were pain free without medication at 10 years. CONCLUSIONS: The literature is limited in its level of evidence, with only one comparative randomized trial (1 vs 2 isocenters) reported to date. At present, one can conclude that RS is a safe and effective therapy for drug-resistant trigeminal neuralgia. A number of consensus statements have been made and endorsed by the ISRS.

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)0.008
metaresearch head score (Gemma)0.031
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 score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.091
GPT teacher head0.386
Teacher spread0.294 · 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".

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Citations230
Published2018
Admission routes1
Has abstractyes

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