Evidence map›Paper›PMID 39258876›Full record

ArticleAnnals of medicine2024

Predicting

Yi Li, Xiaomin Xiong, Xiaohua Liu, Mengke Xu, Boping Yang, Xiaoju Li, Yu Li, Bo Lin, Bo Xu

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

9 authors.

Yi LiSchool of Medicine, Chongqing University, Chongqing, China.
Xiaomin XiongSchool of Medicine, Chongqing University, Chongqing, China.
Xiaohua LiuBioengineering College of Chongqing University, Chongqing, China.
Mengke XuChongqing Key Laboratory for Intelligent Oncology in Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China.
Boping YangDepartment of General Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing Health Center for Women and Children, Chongqing, China.
Xiaoju LiDepartment of Pathology, Chongqing University Cancer Hospital and School of Medicine, Chongqing University, Chongqing, China.
Yu LiDepartment of Pathology, Chongqing University Cancer Hospital and School of Medicine, Chongqing University, Chongqing, China.
Bo LinChongqing Key Laboratory for Intelligent Oncology in Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China.
Bo XuSchool of Medicine, Chongqing University, Chongqing, China.ORCID 0000-0001-8693-3060

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe status of

methodsWe included 1695 slides from 1417 patients with ovarian, breast, prostate, and pancreatic cancers across three independent cohorts. Using a self-attention mechanism, we constructed a multi-instance attention model (MIAM) to detect

resultsAcross the four cancer types, MIAM-C outperformed the deep learning-based MIAM in identifying the

conclusionsThe MIAM-C model accurately detected

Indexed as

MutationPoly(ADP-ribose) Polymerase InhibitorsAdultBRCA1 ProteinBRCA2 ProteinBreast NeoplasmsFemaleHumansMaleMiddle AgedMolecular Targeted TherapyOvarian NeoplasmsPancreatic NeoplasmsPrognosisProgression-Free SurvivalBRCA1 ProteinBRCA1 protein, humanBRCA2 ProteinBRCA2 protein, humanPoly(ADP-ribose) Polymerase InhibitorsBRCAcancerdeep learninginterpretabilitymultimodaltargeted therapy

Identifiers

PMID39258876
PMCPMC11391871

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.