Evidence map›Paper›PMID 33810544›Full record

ReviewCancers2021

The Evolving Role of Marked Lymph Node Biopsy (MLNB) and Targeted Axillary Dissection (TAD) after Neoadjuvant Chemotherapy (NACT) for Node-Positive Breast Cancer: Systematic Review and Pooled Analysis.

Parinita K Swarnkar, Salim Tayeh, Michael J Michell, Kefah Mokbel

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 1 pooled it
5.4field-weighted citation impact, top 3% 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

56 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.

  1. Pooled it
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  3. Observational
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  18. Observational
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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

4 authors at 1 institution in 1 country.

Parinita K SwarnkarThe London Breast Institute, The Princess Grace Hospital, London W1U 5NY, UK.ORCID 0000-0002-3982-3173
Salim TayehThe London Breast Institute, The Princess Grace Hospital, London W1U 5NY, UK.
Michael J MichellThe London Breast Institute, The Princess Grace Hospital, London W1U 5NY, UK.
Kefah MokbelThe London Breast Institute, The Princess Grace Hospital, London W1U 5NY, UK.
The Princess Grace Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Targeted axillary dissection (TAD) is a new axillary staging technique that consists of the surgical removal of biopsy-proven positive axillary nodes, which are marked (marked lymph node biopsy (MLNB)) prior to neoadjuvant chemotherapy (NACT) in addition to the sentinel lymph node biopsy (SLNB). In a meta-analysis of more than 3000 patients, we previously reported a false-negative rate (FNR) of 13% using the SLNB alone in this setting. The aim of this systematic review and pooled analysis is to determine the FNR of MLNB alone and TAD (MLNB plus SLNB) compared with the gold standard of complete axillary lymph node dissection (cALND). The PubMed, Cochrane and Google Scholar databases were searched using MeSH-relevant terms and free words. A total of 9 studies of 366 patients that met the inclusion criteria evaluating the FNR of MLNB alone were included in the pooled analysis, yielding a pooled FNR of 6.28% (95% CI: 3.98-9.43). In 13 studies spanning 521 patients, the addition of SLNB to MLNB (TAD) was associated with a FNR of 5.18% (95% CI: 3.41-7.54), which was not significantly different from that of MLNB alone (

Indexed as

breast cancernode positivesystematic reviewtargeted axillary dissection

Identifiers

PMID33810544
PMCPMC8037051
OpenAlexW3135558121

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.