Large Language Models for Supporting Clear Writing and Detecting Spin in Randomized Controlled Trials in Oncology Article Swipe

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Carole Koechli , Fabio Dennstädt , Christina Schröder , Daniel M. Aebersold , Robert Förster , Daniel R. Zwahlen , Paul Windisch ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1101/2025.05.05.25326991

Importance Accurate interpretation of randomized controlled trial (RCT) results is essential for guiding clinical practice in oncology. Reporting “spin” can misrepresent treatment efficacy, potentially leading to suboptimal clinical decisions. Standardized methods could help to detect misleading reporting. Objective To determine whether large language models (LLMs) can accurately classify oncology RCTs as positive or negative based on primary endpoint achievement when provided with different sections of the trial report, thereby assessing their utility in identifying potential spin in conclusions. Design Methodological evaluation using LLMs and human annotations of previously published clinical trials. Setting Random sample of RCTs from seven major medical journals published between 2005 and 2023. Participants 250 two-arm, single primary endpoint oncology RCT reports were randomly selected from the specified journals and publication years. Exposure(s) Human annotators independently classified trials based on primary endpoint results before LLM evaluation. Three commercial LLMs (GPT-3.5 Turbo, GPT-4o, and o1) classified trials based on four different text inputs: 1) conclusion only, 2) methods and conclusion, 3) methods, results, and conclusion, or 4) title and full abstract. Main Outcome(s) and Measure(s) Performance of LLMs in classifying trials as positive or negative, primarily measured using the F1 score. Results The analysis included 250 RCT reports; based on human annotation, 146 (58.4%) were positive and 104 (41.6%) were negative. o1 demonstrated the highest performance across all input conditions, achieving F1 scores of 0.932 (conclusion only), 0.960 (methods and conclusion), 0.980 (methods, results, and conclusion), and 0.970 (title and full abstract). Analysis of trials incorrectly classified as positive by the LLM when using only the conclusion revealed patterns such as absence of primary endpoint data, emphasis on secondary or subgroup findings, or unclear endpoint distinctions within the conclusion. Conclusions and Relevance LLMs can accurately classify oncology RCT outcomes. Discrepancies between classifications based on conclusions versus more complete text indicate potential spin. This approach could serve as a valuable supplementary tool for researchers, reviewers, and editors to enhance transparency and critical appraisal of oncology trial reporting, though further validation is required, especially for trials with more complex designs. Key Points Question Can large language models (LLMs) accurately classify oncology randomized controlled trials (RCTs) as positive or negative based on primary endpoint achievement, and can this help identify potential “spin” in trial conclusions? Findings In this methodological evaluation of 250 oncology RCTs, the o1 LLM achieved high accuracy in classifying trials based on the title and full abstract, outperforming classifications based on the conclusion alone. Discrepancies between classifications using conclusions versus more complete text often indicated patterns that could be considered as spin. Meaning LLMs show promise as a supplementary tool to detect potential spin by identifying inconsistencies between conclusions and overall results.

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Randomized Controlled Trial

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Randomized controlled trial Oncology Internal medicine Medical physics Medicine Computer science

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1101/2025.05.05.25326991
PDF: https://www.medrxiv.org/content/medrxiv/early/2025/05/06/2025.05.05.25326991.full.pdf
OA Status: green
References: 17
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OpenAlex ID: https://openalex.org/W4410110677

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DOI: https://doi.org/10.1101/2025.05.05.25326991

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Title: Large Language Models for Supporting Clear Writing and Detecting Spin in Randomized Controlled Trials in Oncology

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-05-06

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Authors: Carole Koechli, Fabio Dennstädt, Christina Schröder, Daniel M. Aebersold, Robert Förster, Daniel R. Zwahlen, Paul Windisch

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Landing page: https://doi.org/10.1101/2025.05.05.25326991

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Concepts: Randomized controlled trial, Oncology, Internal medicine, Medical physics, Medicine, Computer science

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abstract_inverted_index.abstract.	173
abstract_inverted_index.achieving	223
abstract_inverted_index.appraisal	324
abstract_inverted_index.assessing	70
abstract_inverted_index.determine	40
abstract_inverted_index.different	63, 153
abstract_inverted_index.efficacy,	23
abstract_inverted_index.essential	11
abstract_inverted_index.findings,	274
abstract_inverted_index.indicated	417
abstract_inverted_index.negative,	187
abstract_inverted_index.negative.	213
abstract_inverted_index.oncology.	17
abstract_inverted_index.outcomes.	291
abstract_inverted_index.potential	75, 303, 370, 435
abstract_inverted_index.primarily	188
abstract_inverted_index.published	89, 102
abstract_inverted_index.required,	333
abstract_inverted_index.secondary	271
abstract_inverted_index.specified	121
abstract_inverted_index.treatment	22
abstract_inverted_index.Importance	1
abstract_inverted_index.Measure(s)	177
abstract_inverted_index.Outcome(s)	175
abstract_inverted_index.abstract).	244
abstract_inverted_index.accurately	47, 287, 349
abstract_inverted_index.annotators	128
abstract_inverted_index.classified	130, 148, 249
abstract_inverted_index.commercial	141
abstract_inverted_index.conclusion	157, 259, 405
abstract_inverted_index.considered	422
abstract_inverted_index.controlled	6, 353
abstract_inverted_index.decisions.	29
abstract_inverted_index.especially	334
abstract_inverted_index.evaluation	81, 379
abstract_inverted_index.misleading	36
abstract_inverted_index.previously	88
abstract_inverted_index.randomized	5, 352
abstract_inverted_index.reporting,	328
abstract_inverted_index.reporting.	37
abstract_inverted_index.reviewers,	316
abstract_inverted_index.suboptimal	27
abstract_inverted_index.validation	331
abstract_inverted_index.“spin”	19, 371
abstract_inverted_index.(conclusion	228
abstract_inverted_index.Conclusions	282
abstract_inverted_index.Exposure(s)	126
abstract_inverted_index.Performance	178
abstract_inverted_index.achievement	59
abstract_inverted_index.annotation,	204
abstract_inverted_index.annotations	86
abstract_inverted_index.classifying	182, 391
abstract_inverted_index.conclusion,	162, 167
abstract_inverted_index.conclusion.	281
abstract_inverted_index.conclusions	297, 411, 441
abstract_inverted_index.conditions,	222
abstract_inverted_index.evaluation.	139
abstract_inverted_index.identifying	74, 438
abstract_inverted_index.incorrectly	248
abstract_inverted_index.performance	218
abstract_inverted_index.potentially	24
abstract_inverted_index.publication	124
abstract_inverted_index.Participants	107
abstract_inverted_index.Standardized	30
abstract_inverted_index.achievement,	364
abstract_inverted_index.conclusion),	233, 238
abstract_inverted_index.conclusions.	78
abstract_inverted_index.conclusions?	374
abstract_inverted_index.demonstrated	215
abstract_inverted_index.distinctions	278
abstract_inverted_index.misrepresent	21
abstract_inverted_index.researchers,	315
abstract_inverted_index.transparency	321
abstract_inverted_index.Discrepancies	292, 407
abstract_inverted_index.independently	129
abstract_inverted_index.outperforming	400
abstract_inverted_index.supplementary	312, 431
abstract_inverted_index.Methodological	80
abstract_inverted_index.interpretation	3
abstract_inverted_index.methodological	378
abstract_inverted_index.classifications	294, 401, 409
abstract_inverted_index.inconsistencies	439
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	7
citation_normalized_percentile.value	0.21858282
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False