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Record W4225098767 · doi:10.2196/35243

Lymph Node Yield in Gastrointestinal Cancer Surgery With or Without Prior Neoadjuvant Therapy: Protocol for a Systematic Review and Meta-analysis

2022· review· en· W4225098767 on OpenAlexvenueno aff
Ulrich Ronellenfitsch, Nika Mathis, Juliane Friedrichs, Jörg Kleeff

Bibliographic record

VenueJMIR Research Protocols · 2022
Typereview
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeoadjuvant therapyLymph nodeRadiation therapyOncologyEsophageal cancerChemoradiotherapyColorectal cancerEsophagectomyCarcinomaEsophagusLymphPancreatic cancerCancerRadiologySurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Lymph node yield is the number of lymph nodes retrieved during oncological resection and histopathologically identified in the resection specimen. It is an important surrogate parameter for assessing the oncological radicality of the resection of gastrointestinal carcinomas, as well as a prognostic factor in these diseases. It remains unclear if and to what extent neoadjuvant chemotherapy, radiotherapy, or chemoradiotherapy, which have become established treatments for carcinoma of the esophagus, stomach, and rectum and are increasingly used in pancreatic carcinoma, affect the lymph node yield. OBJECTIVE: This systematic review with meta-analysis is conducted with the aim of summarizing the available evidence regarding the lymph node yield, an oncological surrogate marker, in patients with gastrointestinal carcinomas undergoing surgery after neoadjuvant therapy compared to those undergoing surgery without neoadjuvant therapy. METHODS: Randomized and nonrandomized studies comparing oncological resection of esophageal, stomach, pancreatic, and rectal carcinoma with and without prior neoadjuvant therapy are eligible for inclusion regardless of study design. Publications will be identified with a defined search strategy in 2 electronic databases: PubMed and Cochrane Library. The primary endpoint of the analysis is the number of lymph nodes identified in the resected specimen. Secondary endpoints include the number of harvested metastatic lymph nodes, operation time, postoperative complications, pathological TNM staging, and overall and recurrence-free survival time. Using suitable statistical methods, the endpoints between patients with and without neoadjuvant therapy, as well as in defined subgroups (neoadjuvant chemotherapy, radiotherapy, or chemoradiotherapy; and patients with esophageal, gastric, pancreatic, or rectal cancer), will be compared. RESULTS: The literature search and data collection started in October 2021. Results are expected to be published in mid-2022. CONCLUSIONS: This meta-analysis will provide the most up-to-date and complete summary of the evidence on an association between neoadjuvant therapy and lymph node yield in gastrointestinal cancer surgery. The underlying hypothesis is that neoadjuvant therapy decreases the number and size of lymph nodes through lymphocyte depletion and radiation-induced fibrosis, thus leading to a lower possible lymph node yield. The findings of the meta-analysis will show if this hypothesis is supported by evidence. TRIAL REGISTRATION: PROSPERO CRD218459; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021218459. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35243.

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)0.036
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.036
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.060
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0210.032
Bibliometrics0.0080.009
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0040.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0290.002

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 head0.596
GPT teacher head0.613
Teacher spread0.017 · 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 designMeta-analysis
Domainnot available
GenreProtocol

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

Citations1
Published2022
Admission routes1
Has abstractyes

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