Evidence map›Paper›PMID 37866919›Full record

ArticleInternal medicine (Tokyo, Japan)2024

Usefulness of Quantification and Serial Monitoring of Fine Crackles for Early Detection of Treatment-related Lung Injury: A Report of Two Cases.

Kanako Nakamoto, Yasushi Horimasu, Kakuhiro Yamaguchi, Shinjiro Sakamoto, Takeshi Masuda, Shintaro Miyamoto, Taku Nakashima, Hiroshi Iwamoto, Shinichiro Ohshimo, Takuma Sadamori and 4 more

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In one paragraph

Article in Internal medicine (Tokyo, Japan), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Kanako NakamotoDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Yasushi HorimasuDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Kakuhiro YamaguchiDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Shinjiro SakamotoDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Takeshi MasudaDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Shintaro MiyamotoDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Taku NakashimaDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Hiroshi IwamotoDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Shinichiro OhshimoDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan.
Takuma SadamoriDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan.
Kazunori FujitakaDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Hironobu HamadaDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.
Nobuaki ShimeDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan.
Noboru HattoriDepartment of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection and appropriate management of treatment-related interstitial lung disease (ILD) are important in cancer treatment. We established an algorithm for quantifying fine crackles using machine learning and reported that the fine crackle quantitative value (FCQV) calculated by this algorithm was more sensitive than chest radiography for detecting interstitial changes. Using this algorithm, we periodically analyzed respiratory sounds in two patients with lung cancer who developed treatment-related ILDs and found that the FCQV was elevated before the diagnosis of ILD. These cases may indicate the usefulness of the FCQV in the early diagnosis of treatment-related ILDs.

Indexed as

Early DiagnosisLung Diseases, InterstitialLung NeoplasmsRespiratory SoundsAgedAlgorithmsFemaleHumansLung InjuryMaleMiddle Agedauscultationlung neoplasmspneumoniastethoscopes

Identifiers

PMID37866919
PMCPMC11239243

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.