Evidence map›Paper›PMID 24326463›Full record

ReviewApplied immunohistochemistry & molecular morphology : AIMM2014

Practicing pathology in the era of big data and personalized medicine.

Jiang Gu, Clive R Taylor

Open access · greenAbstract readReview
In one paragraph

Review in Applied immunohistochemistry & molecular morphology : AIMM, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 59 citations in OpenAlex.

  1. Article
  2. Review
  3. A Topological Comparison of the Fluorescence Imitating Brightfield Imaging and H&E Imaging.Topology- and graph-informed imaging informatics : first International Workshop, TGI3 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings. International Workshop on Topology- and Graph-Informed ... · 2025
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Applications of Topological Data Analysis in Oncology.Frontiers in artificial intelligence · 2021
    Review
  9. Article
  10. 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 2 countries.

Jiang Gu*Department of Pathology, Shantou University Medical College, Shantou, Guangdong, China †Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA.
Clive R Taylor
Shantou University Medical College · CNUniversity of Southern California · US

Funding

Retrieval of DNA, RNA, and Protein from Archival TissueR33CA103455 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TAYLOR, CLIVE R · 2003 to 2005
$1.8M
NCI NIH HHS R33 CA103455
6 · The paper itself

Abstract

The traditional task of the pathologist is to assist physicians in making the correct diagnosis of diseases at the earliest possible stage to effectuate the optimal treatment strategy for each individual patient. In this respect surgical pathology (the traditional tissue diagnosis) is but a tool. It is not, of itself, the purpose of pathology practice; and change is in the air. This January 2014 issue of Applied Immunohistochemistry and Molecular Morphology (AIMM) embraces that change by the incorporation of the agenda and content of the journal Diagnostic Molecular Morphology (DMP). Over a decade ago AIMM introduced and promoted the concept of "molecular morphology," and has sought to publish molecular studies that correlate with the morphologic features that continue to define cancer and many diseases. That intent is now reinforced and extended by the merger with DMP, as a logical and timely response to the growing impact of a wide range of genetic and molecular technologies that are beginning to reshape the way in which pathology is practiced. The use of molecular and genomic techniques already demonstrates clear value in the diagnosis of disease, with treatment tailored specifically to individual patients. Personalized medicine is the future, and personalized medicine demands personalized pathology. The need for integration of the flood of new molecular data, with surgical pathology, digital pathology, and the full range of pathology data in the electronic medical record has never been greater. This review describes the possible impact of these pressures upon the discipline of pathology, and examines possible outcomes. There is a sense of excitement and adventure. Active adaption and innovation are required. The new AIMM, incorporating DMP, seeks to position itself for a central role in this process.

Indexed as

PathologyPrecision MedicineHumansNeoplasms

Identifiers

PMID24326463
PMCPMC4206549
OpenAlexW2023378387

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.