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Record W2957932239 · doi:10.21037/jgo.2019.06.09

Clinical outcomes of endoscopic submucosal tunnel dissection compared with conventional endoscopic submucosal dissection for superficial esophageal cancer: a systematic review and meta-analysis

2019· review· en· W2957932239 on OpenAlexaboutno aff
Jiaxi Lu, Deliang Liu, Yuyong Tan

Bibliographic record

VenueJournal of Gastrointestinal Oncology · 2019
Typereview
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEndoscopic submucosal dissectionEsophageal cancerCochrane LibraryMeta-analysisAdverse effectDissection (medical)SurgeryCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Endoscopic submucosal dissection (ESD) is the standard treatment for superficial esophageal cancer. However, it has limitations in case of large superficial esophageal squamous cell neoplasms (SESCNs), in which submucosal injection cannot attain satisfactory lifting effects. Thus, endoscopic submucosal tunnel dissection (ESTD) was introduced as a new treatment for SESCNs presenting satisfying results. Many studies have tried to verify the efficacy of ESTD, yet no meta-analysis has been published until now. METHODS: We searched the databases of PubMed, Cochrane Library, Web of Science, SinoMed, Wanfang, and CNKI dating up to February 1, 2019. Studies comparing the clinical outcomes of ESTD and ESD for superficial esophageal cancers were enrolled. The Newcastle-Ottawa Quality Assessment Scale was used to evaluate the quality of these studies. Eight articles were included that involved a total of 625 superficial esophageal cancer patients. RESULTS: resection rate, shorter operation time, and lower recurrence rate 1 year after operation. The R0 resection rate and postoperative adverse event rate of ESTD group is comparable with ESD group. CONCLUSIONS: Our study implicates that ESTD is a potentially superior treatment to ESD for superficial esophageal cancer.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0160.005
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.153
GPT teacher head0.468
Teacher spread0.315 · 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.

Study designMeta-analysis
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

Citations10
Published2019
Admission routes1
Has abstractyes

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