Preliminary Evaluation of a Large Language Model-Based Tool for Complex Surgical Decision Support in Lung Cancer
Aperçu de l'étude
Statut
Statut
Les conditions
Les conditions
Intervention / Traitement
Intervention / Traitement
Type d'étude
Type d'étude
Inscription (Réel)
Inscription
Phase
Phase
- N'est pas applicable
Contacts et emplacements
Lieux d'étude
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Beijing Municipality
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Beijing, Beijing Municipality, Chine, 100044
- Peking University People's Hospital
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Critères de participation
Critère d'éligibilité
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
La description
Inclusion Criteria:
Resident Physician Subjects:
- Holds a valid and legally effective Physician Practice License of the People's Republic of China;
- Currently holds the rank of resident physician in a thoracic surgery department at a tertiary Class A (3A) hospital;
- Agrees to complete all assessment tasks of the main study phase in accordance with the study protocol;
- Can guarantee the time and effort required to complete all assessment tasks of the main study.
Study Cases:
- The case was discussed at the Thoracic Oncology Multidisciplinary Team (MDT) conference of Peking University People's Hospital between January 2025 and May 2026;
- The current version of the NCCN guidelines does not provide an explicit recommendation covering the management of the case;
- Does not overlap with the GAPS evaluation set;
- The case is presented in pure text in a structured format, with all direct and indirect identifiers removed and complete de-identification performed prior to inclusion;
- From the pool of eligible cases, 12 cases will be randomly drawn using Python (numpy.random, with a fixed and archived seed) to serve as the main study cases. The cases will cover 6 themes (chest mass of undetermined diagnosis, early-stage lung cancer, locally advanced lung cancer, oligometastatic/oligoprogressive disease, special intraoperative situations, and tumor recurrence), with 1 - 4 cases per theme.
Adjudication Expert Panel:
- Holds a valid and legally effective Physician Practice License of the People's Republic of China;
- Currently holds the rank of attending physician or above in a thoracic surgery department at a tertiary Class A hospital;
- Chairs or regularly participates in lung cancer multidisciplinary team (MDT) work in their department.
Exclusion Criteria:
Resident Physician Subjects:
- Has previously participated in the construction of the GAPS evaluation set or the development of GAPS-Agent;
- Unable to complete the tasks of the study phase.
Study Cases:
- Key case information is missing, such as text-form data on pathology (including IHC/NGS), imaging, laboratory tests, prior medical history, comorbidities, or PS score;
- Decision-making for the case is strictly dependent on non-text information.
Adjudication Expert Panel:
- Participated in the construction of the GAPS evaluation set, the content validity verification, or the development of GAPS-Agent for this study;
- Has a direct conflict of interest with any specific product among the two-arm tools of this study.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Autre
- Répartition: Randomisé
- Modèle interventionnel: Affectation parallèle
- Masquage: Seul
Nombre de bras
Armes et Interventions
Groupe de participants / BrasGroupe de participants / Bras |
Intervention / TraitementIntervention / Traitement |
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Expérimental: test arm
GAPS-Agent
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The research group has previously developed the GAPS evaluation framework for complex clinical decision-making in lung cancer.
In this framework, G (Grounding) characterizes the cognitive depth of decision-making (ranging from knowledge retrieval to decisions that go beyond clinical guidelines), A (Authority) corresponds to the grading of evidence strength, P (Perturbation) describes the identification and management of real-world clinical confounding factors, and S (Strength) corresponds to the calibration of recommendation strength.
Within this framework, the research group has completed the construction of a 100-item complex lung cancer decision-making evaluation set along with its corresponding rubrics, and has invited multiple thoracic oncology experts to complete content validity validation.
Based on this, the research group developed GAPS-Agent, which uses an open-source large language model as its foundation and integrates functional modules such as guideline and evidence retri
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Comparateur actif: control arm
LLM
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Open source large language model that is not specifically enhanced in medical field.
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Que mesure l'étude ?
Principaux critères de jugement
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
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Overall plan Win Ratio
Délai: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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A total of 10 blinded expert judges made Win/Tie/Loss ternary preference judgments on 192 paired scheme comparisons in terms of overall scheme quality.
The win ratio was calculated as Wins ÷ Losses, and the 95% confidence interval was estimated using a two-level (physician × case) cluster bootstrap resampling method (B = 10,000, quantile method on the log scale).
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Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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Mesures de résultats secondaires
Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Inter-rater agreement
Délai: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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For the ternary preference judgment results of 10 expert judges across 192 paired comparisons and 6 evaluation domains, Fleiss' kappa was used to assess inter-rater agreement.
The kappa value and its 95% confidence interval are reported for each evaluation domain.
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Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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Redundancy Win Ratio
Délai: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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A total of 10 blinded expert judges made Win/Tie/Loss ternary preference judgments on 192 paired scheme comparisons in terms of overall scheme quality.
The win ratio was calculated as Wins ÷ Losses, and the 95% confidence interval was estimated using a two-level (physician × case) cluster bootstrap resampling method (B = 10,000, quantile method on the log scale).
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Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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Evidence-based medicine adherence Win Ratio
Délai: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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A total of 10 blinded expert judges made Win/Tie/Loss ternary preference judgments on 192 paired scheme comparisons in terms of overall scheme quality.
The win ratio was calculated as Wins ÷ Losses, and the 95% confidence interval was estimated using a two-level (physician × case) cluster bootstrap resampling method (B = 10,000, quantile method on the log scale).
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Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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Actionability Win Ratio
Délai: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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A total of 10 blinded expert judges made Win/Tie/Loss ternary preference judgments on 192 paired scheme comparisons in terms of overall scheme quality.
The win ratio was calculated as Wins ÷ Losses, and the 95% confidence interval was estimated using a two-level (physician × case) cluster bootstrap resampling method (B = 10,000, quantile method on the log scale).
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Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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Completeness Win Ratio
Délai: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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A total of 10 blinded expert judges made Win/Tie/Loss ternary preference judgments on 192 paired scheme comparisons in terms of overall scheme quality.
The win ratio was calculated as Wins ÷ Losses, and the 95% confidence interval was estimated using a two-level (physician × case) cluster bootstrap resampling method (B = 10,000, quantile method on the log scale).
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Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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Safety Win Ratio
Délai: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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A total of 10 blinded expert judges made Win/Tie/Loss ternary preference judgments on 192 paired scheme comparisons in terms of overall scheme quality.
The win ratio was calculated as Wins ÷ Losses, and the 95% confidence interval was estimated using a two-level (physician × case) cluster bootstrap resampling method (B = 10,000, quantile method on the log scale).
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Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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GAPS automated rubric score
Délai: Generated up to 3 weeks after residents finished their plan generation.
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A third-party large language model, independent of the two study arms' base models, served as the judge model and automatically scored all 96 plans according to the GAPS rubric.
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Generated up to 3 weeks after residents finished their plan generation.
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Subject physician's self-confidence score
Délai: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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After submitting each case plan, the participating physicians self-rated their confidence in their own plan using a 1-5 point Likert scale.
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Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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Tool satisfaction score
Délai: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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After submitting each case plan, the participating physicians rated their satisfaction with the tool using a 1-5 point Likert scale.
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Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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Tool trustworthiness score
Délai: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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After submitting each case plan, the participating physicians rated the tool's credibility using a 1-5 point Likert scale.
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Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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Decision-making time
Délai: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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The time taken (in minutes) by each participating physician to complete the production of each case plan was automatically recorded by the evaluation platform.
Differences between groups were analyzed using a linear mixed-effects model.
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Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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Collaborateurs et enquêteurs
Parrainer
Parrainer
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Réel)
Début de l'étude
Achèvement primaire (Réel)
Achèvement primaire
Achèvement de l'étude (Réel)
Achèvement de l'étude
Dates d'inscription aux études
Première soumission
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Première publication
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour publiée
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Dernière vérification
Plus d'information
Termes liés à cette étude
Mots clés
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
Autres numéros d'identification d'étude
- 2026PHB458-001
Plan pour les données individuelles des participants (IPD)
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Informations sur les médicaments et les dispositifs, documents d'étude
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