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Preliminary Evaluation of a Large Language Model-Based Tool for Complex Surgical Decision Support in Lung Cancer

lauantai 13. kesäkuuta 2026 päivittänyt: XiuYuan Chen, Peking University People's Hospital
This study is an exploratory effect-size estimation study, with the following specific objectives: ① to estimate the point estimate and 95% confidence interval of the Win Ratio for the experimental group (GAPS-Agent) versus the control group (large language model) in blinded pairwise preference judgments by thoracic surgery expert adjudicators, to serve as a sample size planning parameter for subsequent multicenter confirmatory clinical trials; ② to preliminarily evaluate the value of GAPS-Agent within clinical workflows.The hypothesis of this study is as follows: compared with a general-purpose large language model without medical enhancement (control group), a structured agentic workflow optimized on the basis of the GAPS evaluation framework (GAPS-Agent, experimental group) can help junior resident physicians generate clinical decision plans for complex lung cancer cases that are more strongly preferred by senior thoracic surgery expert adjudicators.

Tutkimuksen yleiskatsaus

Tila

Ilmoittautuminen kutsusta

Ehdot

Interventio / Hoito

Opintotyyppi

Interventio

Ilmoittautuminen (Arvioitu)

12

Vaihe

  • Ei sovellettavissa

Yhteystiedot ja paikat

Tässä osiossa on tutkimuksen suorittajien yhteystiedot ja tiedot siitä, missä tämä tutkimus suoritetaan.

Opiskelupaikat

  • Kiina
    • Beijing Municipality
      • Beijing, Beijing Municipality, Kiina, 100044
        • Peking University People's Hospital

Osallistumiskriteerit

Tutkijat etsivät ihmisiä, jotka sopivat tiettyyn kuvaukseen, jota kutsutaan kelpoisuuskriteereiksi. Joitakin esimerkkejä näistä kriteereistä ovat henkilön yleinen terveydentila tai aiemmat hoidot.

Kelpoisuusvaatimukset

Opintokelpoiset iät

  • Aikuinen
  • Vanhempi Aikuinen

Hyväksyy terveitä vapaaehtoisia

Ei

Kuvaus

Inclusion Criteria:

  1. Resident Physician Subjects:

    1. Holds a valid and legally effective Physician Practice License of the People's Republic of China;
    2. Currently holds the rank of resident physician in a thoracic surgery department at a tertiary Class A (3A) hospital;
    3. Agrees to complete all assessment tasks of the main study phase in accordance with the study protocol;
    4. Can guarantee the time and effort required to complete all assessment tasks of the main study.

  2. Study Cases:

    1. The case was discussed at the Thoracic Oncology Multidisciplinary Team (MDT) conference of Peking University People's Hospital between January 2025 and May 2026;
    2. The current version of the NCCN guidelines does not provide an explicit recommendation covering the management of the case;
    3. Does not overlap with the GAPS evaluation set;
    4. 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;
    5. 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 2 cases per theme.

  3. Adjudication Expert Panel:

    1. Holds a valid and legally effective Physician Practice License of the People's Republic of China;
    2. Currently holds the rank of attending physician or above in a thoracic surgery department at a tertiary Class A hospital;
    3. Chairs or regularly participates in lung cancer multidisciplinary team (MDT) work in their department.

Exclusion Criteria:

  1. Resident Physician Subjects:

    1. Has previously participated in the construction of the GAPS evaluation set or the development of GAPS-Agent;
    2. Unable to complete the tasks of the study phase.

  2. Study Cases:

    1. 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;
    2. Decision-making for the case is strictly dependent on non-text information.

  3. Adjudication Expert Panel:

    1. Participated in the construction of the GAPS evaluation set, the content validity verification, or the development of GAPS-Agent for this study;
    2. Has a direct conflict of interest with any specific product among the two-arm tools of this study.

Opintosuunnitelma

Tässä osiossa on tietoja tutkimussuunnitelmasta, mukaan lukien kuinka tutkimus on suunniteltu ja mitä tutkimuksella mitataan.

Miten tutkimus on suunniteltu?

Suunnittelun yksityiskohdat

  • Ensisijainen käyttötarkoitus: Muut
  • Jako: Satunnaistettu
  • Inventiomalli: Rinnakkaistehtävä
  • Naamiointi: Yksittäinen

Aseet ja interventiot

Osallistujaryhmä / Arm
Interventio / Hoito
Kokeellinen: test arm
GAPS-Agent
Muut: GAPS-Agent
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
Active Comparator: control arm
LLM
Muut: LLM
Open source large language model that is not specifically enhanced in medical field.

Mitä tutkimuksessa mitataan?

Ensisijaiset tulostoimenpiteet

Tulosmittaus
Toimenpiteen kuvaus
Aikaikkuna
Overall plan Win Ratio
Aikaikkuna: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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).
Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.

Toissijaiset tulostoimenpiteet

Tulosmittaus
Toimenpiteen kuvaus
Aikaikkuna
Inter-rater agreement
Aikaikkuna: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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.
Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
Redundancy Win Ratio
Aikaikkuna: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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).
Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
Evidence-based medicine adherence Win Ratio
Aikaikkuna: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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).
Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
Actionability Win Ratio
Aikaikkuna: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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).
Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
Completeness Win Ratio
Aikaikkuna: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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).
Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
Safety Win Ratio
Aikaikkuna: Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
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).
Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.
GAPS automated rubric score
Aikaikkuna: Generated up to 3 weeks after residents finished their plan generation.
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.
Generated up to 3 weeks after residents finished their plan generation.
Subject physician's self-confidence score
Aikaikkuna: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
After submitting each case plan, the participating physicians self-rated their confidence in their own plan using a 1-5 point Likert scale.
Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
Tool satisfaction score
Aikaikkuna: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
After submitting each case plan, the participating physicians rated their satisfaction with the tool using a 1-5 point Likert scale.
Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
Tool trustworthiness score
Aikaikkuna: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
After submitting each case plan, the participating physicians rated the tool's credibility using a 1-5 point Likert scale.
Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
Decision-making time
Aikaikkuna: Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.
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.
Completed at the time when residents submitted their plans. Calculated up to 3 weeks after the submission.

Yhteistyökumppanit ja tutkijat

Täältä löydät tähän tutkimukseen osallistuvat ihmiset ja organisaatiot.

Sponsori

Peking University People's Hospital

Opintojen ennätyspäivät

Nämä päivämäärät seuraavat ClinicalTrials.gov-sivustolle lähetettyjen tutkimustietueiden ja yhteenvetojen edistymistä. National Library of Medicine (NLM) tarkistaa tutkimustiedot ja raportoidut tulokset varmistaakseen, että ne täyttävät tietyt laadunvalvontastandardit, ennen kuin ne julkaistaan ​​julkisella verkkosivustolla.

Opi tärkeimmät päivämäärät

Opiskelun aloitus (Todellinen)

Keskiviikko 10. kesäkuuta 2026

Ensisijainen valmistuminen (Arvioitu)

Sunnuntai 21. kesäkuuta 2026

Opintojen valmistuminen (Arvioitu)

Sunnuntai 21. kesäkuuta 2026

Opintoihin ilmoittautumispäivät

Ensimmäinen lähetetty

Keskiviikko 10. kesäkuuta 2026

Ensimmäinen toimitettu, joka täytti QC-kriteerit

Lauantai 13. kesäkuuta 2026

Ensimmäinen Lähetetty (Todellinen)

Keskiviikko 17. kesäkuuta 2026

Tutkimustietojen päivitykset

Viimeisin päivitys julkaistu (Todellinen)

Keskiviikko 17. kesäkuuta 2026

Viimeisin lähetetty päivitys, joka täytti QC-kriteerit

Lauantai 13. kesäkuuta 2026

Viimeksi vahvistettu

Maanantai 1. kesäkuuta 2026

Lisää tietoa

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Avainsanat

Muita asiaankuuluvia MeSH-ehtoja

Muut tutkimustunnusnumerot

  • 2026PHB458-001

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Aiotko jakaa yksittäisten osallistujien tietoja (IPD)?

EI

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