Evidence map›Paper›PMID 25562202›Full record

ReviewJournal of personalized medicine2012

Challenges in Implementing Personalized Medicine for Lung Cancer within a National Healthcare System.

David E Dawe, Peter M Ellis

Open access · goldAbstract readReview
In one paragraph

Review in Journal of personalized medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Cost-effectiveness analysis of EGFR mutation testing in patients with non-small cell lung cancer (NSCLC) with gefitinib or carboplatin-paclitaxel.The European journal of health economics : HEPAC : health economics in prevention and care · 2016
    Article
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

2 authors at 2 institutions in 1 country.

David E DaweUniversity of Manitoba, 820 Sherbrook Street, Winnipeg, MB R3A 1R8, Canada. david.dawe@cancercare.mb.ca.
Peter M EllisDepartment of Medical Oncology, McMaster University, Hamilton, ON, L8S 4L8, Canada. peter.ellis@jcc.hhsc.ca.
Juravinski Cancer Centre · CAUniversity of Manitoba · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The traditional approach to the treatment of advanced non-small cell lung cancer (NSCLC) relied on the uniform use of cytotoxic chemotherapy. Over the last eight years, this paradigm of care has been shifting towards the use of molecularly targeted agents. Epidermal growth factor receptor (EGFR) mutations have emerged as an important biomarker for these targeted agents and multiple studies have shown that tyrosine kinase inhibitors (TKI) that inhibit EGFR are superior to traditional chemotherapy in patients possessing an EGFR mutation. Nationally funded health care systems face a number of challenges in implementing these targeted therapies, most related to the need to test for biomarkers that predict likelihood of benefiting from the drug. These obstacles include the challenge of getting a large enough tissue sample, workload of involved specialists, reliability of subtyping in NSCLC, differences in biomarker tests, and the disconnect between the funding of drugs and the related biomarker test. In order to improve patient outcomes, in a national healthcare system, there is a need for governments to accept the changing paradigm, invest in technology and build capacity for molecular testing to facilitate the implementation of improved patient care.

Identifiers

PMID25562202
PMCPMC4251366
OpenAlexW2118345793

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.