Evidence map›Paper›PMID 38625588›Full record

SynthesisNeurosurgical review2024

Laser interstitial thermal therapy for recurrent glioblastomas: a systematic review and meta-analysis.

Xuzhe Zhao, Runting Li, Yiding Guo, Haibin Wan, Dabiao Zhou

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Neurosurgical review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Xuzhe ZhaoDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Fanyang Road, Fengtai District, Beijing, 100070, China.
Runting LiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Fanyang Road, Fengtai District, Beijing, 100070, China.
Yiding GuoDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Fanyang Road, Fengtai District, Beijing, 100070, China.
Haibin WanDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Fanyang Road, Fengtai District, Beijing, 100070, China.
Dabiao ZhouDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Fanyang Road, Fengtai District, Beijing, 100070, China. dabiaozhou@163.com.
Beijing Tian Tan Hospital · CNCapital Medical University · CN

Funding

National Natural Science Foundation of China 82151310
6 · The paper itself

Abstract

We aim to investigate the efficacy and safety of laser interstitial thermal therapy (LITT) in treating recurrent glioblastomas (rGBMs). A comprehensive search was conducted in four databases to identify studies published between January 2001 and June 2022 that reported prognosis information of rGBM patients treated with LITT as the primary therapy. The primary outcomes of interest were progression-free survival (PFS) and overall survival (OS) at 6 and 12 months after LITT intervention. Adverse events and complications were also evaluated. Eight eligible non-comparative studies comprising 128 patients were included in the analysis. Seven studies involving 120 patients provided data for the analysis of PFS. The pooled PFS rate at 6 months after LITT was 25% (95% CI 15-37%, I

Indexed as

Brain NeoplasmsGlioblastomaLaser TherapyNeoplasm Recurrence, LocalHumansProgression-Free SurvivalTreatment OutcomeGlioblastomaLaser interstitial thermal therapyLiterature reviewRecurrenceSurvival

Identifiers

PMID38625588
OpenAlexW4394853925

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.