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Record W4415825247 · doi:10.2196/70603

Identifying Terminologies Used Prior to the Onset of Interstitial Lung Disease in Patients With Lung Cancer: Descriptive Analysis of Electronic Medical Record Data

2025· article· en· W4415825247 on OpenAlexvenueno aff
Masami Mukai, Hiroki Adachi, Tomohiro Yamaguchi, Reiko Tanabe, Yasuo Sugitani, Yoshimasa Hanada, Noriaki Nakajima, Naoki Mihara

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
Fundersnot available
KeywordsInterstitial lung diseaseMedical recordLung cancerLungDiseaseElectronic medical recordDescriptive statisticsCancer

Abstract

fetched live from OpenAlex

Background: The growing importance of real-world data (RWD) as a source of evidence for drug effects has led to increased interest in clinical research utilizing secondary use data from electronic medical record systems. Although immune checkpoint inhibitors and targeted therapies have advanced lung cancer treatment, managing complications such as interstitial lung disease (ILD) remains challenging. Early detection and prevention of ILD are crucial for improving patient prognosis and quality of life; however, predictive biomarkers have yet to be established. Therefore, methods to identify ILD risk factors and enable early detection using RWD are needed. Objective: This exploratory study aimed to identify associated factors and prodromal symptoms of ILD onset using clinical data stored in a hospital information system. Methods: Clinical data of patients diagnosed with stage IV lung cancer between November 2011 and December 2018 were extracted from the hospital information system of the National Cancer Center Hospital in Japan. A total of 3 patient groups were defined: the ILD Set, based on laboratory test results and radiological records; the ILD-GC Set, which added glucocorticoid treatment to the ILD Set; and the No ILD Set, for patients without ILD. The primary endpoint was the frequency of Japanese words extracted from electronic medical records, specifically from notes in the Problem-Oriented System/Subjective, Objective, Assessment and Plan format. Noun frequencies were compared between the ILD or ILD-GC Sets and the No ILD Set. Free-text data were processed using morphological analysis, and terms were categorized using the Patient Disease Expression Dictionary or the World Health Organization Drug Dictionary. Key terms were extracted from physician and nurse records based on the descending order of ranking differences to identify associated factors and prodromal symptoms. Results: The analysis included 674 cases (105 in the ILD Set [including 12 in the ILD-GC Set] and 569 in the No ILD Set). Baseline characteristics showed no apparent differences across groups. In the 30 days prior to ILD onset, notable differences in word frequencies per 1000 notes between the ILD-GC Set and No ILD Set were observed in the following term categories: respiratory symptoms (eg, breathlessness, shortness of breath, oxygen), ranging from 170.59 to 46.51; pain or analgesics (eg, Lyrica [pregabalin], soreness, precordial pain, opioids), ranging from 462.88 to 45.16; and appetite-related terms (eg, inappetence, food intake, queasiness, Novamin [prochlorperazine]), ranging from 102.23 to 51.90. Conclusions: Terms related to respiratory symptoms, pain or analgesics, and appetite were identified as associated factors for ILD onset in patients with stage IV lung cancer using RWD from acute care institutions for malignant tumors. These findings may support the early detection of ILD and underscore the potential of RWD to generate real-world evidence that informs drug discovery and pharmaceutical development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.347
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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