Evidence map›Paper›PMID 35588396›Full record

ReviewExpert review of anticancer therapy2022

Addressing the problem of overtreatment in breast cancer.

Linda M Pak, Monica Morrow

Open access · greenAbstract readReview
In one paragraph

Review in Expert review of anticancer therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.0field-weighted citation impact, top 12% 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

17 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Review
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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

2 authors at 1 institution in 1 country.

Linda M PakBreast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York.
Monica MorrowBreast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York.ORCID 0000-0003-4636-8313
Memorial Sloan Kettering Cancer Center · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

introductionAs breast cancer treatment options have multiplied and biologic diversity within breast cancer has been recognized, the use of the same treatment strategies for patients with early-stage and favorable disease, and for those with biologically aggressive disease, has been questioned. In addition, as patient-reported outcome measures have called attention to the morbidity of many common treatments, and as the cost of breast cancer care has continued to increase, reduction in the overtreatment of breast cancer has assumed increasing importance. AREAS COVERED: Here we review selected aspects of surgery, radiation oncology, and medical oncology for which scientific evidence supports de-escalation for invasive carcinoma and ductal carcinoma in situ, and assess strategies to address overtreatment. EXPERT OPINION: The problems of breast cancer overtreatment we face today are based on improved understanding of the biology of breast cancer and abandonment of the 'one-size-fits-all' approach. As breast cancer care becomes increasingly complex, and as our knowledge base continues to increase exponentially, these problems will only be magnified in the future. To continue progress, the move must be made from advocating the maximum-tolerated treatment to advocating the minimum-effective one.

Indexed as

Breast NeoplasmsCarcinoma, Intraductal, NoninfiltratingFemaleHumansMastectomy, SegmentalOvertreatmentaxillabreast cancerbreast-conserving therapychemotherapyductal carcinoma in situovertreatmentradiotherapy

Identifiers

PMID35588396
PMCPMC9448354
OpenAlexW4280618131

What OpenQuestion holds

Textmetadata
LicenceTDM
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.