Evidence map›Paper›PMID 23304085›Full record

ReviewTheScientificWorldJournal2012

Surgical treatment of sporadic pancreatic neuroendocrine tumors: a state of the art review.

Sven-Petter Haugvik, Knut Jørgen Labori, Bjørn Edwin, Øystein Mathisen, Ivar Prydz Gladhaug

Abstract readReview
In one paragraph

Review in TheScientificWorldJournal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Review
  3. Review
  4. Review
  5. Laparoscopic resection of pancreatic neuroendocrine tumors.World journal of gastroenterology · 2014
    Review
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.

Sven-Petter HaugvikDepartment of Hepato-Pancreato-Biliary Surgery, Rikshospitalet, Oslo University Hospital, Sognsvannsveien 20, 0372 Oslo, Norway. svhaug@ous-hf.no
Knut Jørgen Labori
Bjørn Edwin
Øystein Mathisen
Ivar Prydz Gladhaug

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic neuroendocrine tumors (PNETs) are rare neoplasms. They are clinically diverse and divided into functioning and nonfunctioning disease, depending on their ability to produce symptoms due to hormone production. Surgical resection is the only curative treatment and remains the cornerstone therapy for this patient group, even in patients with advanced disease. Over the last decade there has been a noticeable trend towards more aggressive surgery as well as more minimally invasive surgery in patients with PNETs. This has resulted in improved long-term survival in patients with locally advanced and metastatic disease treated aggressively, as well as shorter hospital stays and comparable long-term outcomes in patients with limited disease treated minimally invasively. There are still controversies related to issues of surgical treatment of PNETs, such as to what extent enucleation, lymph node sampling, and vascular reconstruction are beneficial for the oncologic outcome. Histopathologic tumor classification is of high clinical importance for treatment planning and prognostic evaluation of patients with PNETs. A constant challenge, which relates to the treatment of PNETs, is the lack of an internationally accepted histopathological classification system. This paper reviews current issues on the surgical treatment of sporadic PNETs with specific focus on surgical approaches and tumor classification.

Indexed as

AnimalsHumansMinimally Invasive Surgical ProceduresNeuroendocrine TumorsPancreatic Neoplasms

Identifiers

PMID23304085
PMCPMC3523601

What OpenQuestion holds

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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.