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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| test arm | Experimental | GAPS-Agent |
|
| control arm | Active Comparator | LLM |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| GAPS-Agent | Other | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Overall plan Win Ratio | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Inter-rater agreement | 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. |
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Inclusion Criteria:
Resident Physician Subjects:
Study Cases:
Adjudication Expert Panel:
Exclusion Criteria:
Resident Physician Subjects:
Study Cases:
Adjudication Expert Panel:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Peking University People's Hospital | Beijing | Beijing Municipality | 100044 | China |
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| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| D002289 | Carcinoma, Non-Small-Cell Lung |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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| LLM | Other | Open source large language model that is not specifically enhanced in medical field. |
|
| Redundancy Win Ratio | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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. |
| D008171 |
| Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002283 | Carcinoma, Bronchogenic |
| D001984 | Bronchial Neoplasms |