scibert_finetune_rct_consort
Model Details
This is a scibert finetuned model on 50 randomized controlled trial articles annotated with fine-grained CONSORT checklist items at the sentence level.
Model Description
This model classifies input RCT text into the following CONSORT item labels:
- LABEL_1: Any changes to trial outcomes after the trial commenced, with reasons
- LABEL_2: Completely defined pre-specified primary and secondary outcome measures, including how and when they were assessed
- LABEL_3: Description of trial design (such as parallel, factorial) including allocation ratio
- LABEL_4: Eligibility criteria for participants
- LABEL_5: How sample size was determined
- LABEL_6: If done, who was blinded after assignment to interventions (for example, participants, care providers, those assessing outcomes) and how
- LABEL_7: If relevant, description of the similarity of interventions
- LABEL_8: Important changes to methods after trial commencement (such as eligibility criteria), with reasons
- LABEL_9: Mechanism used to implement the random allocation sequence (such as sequentially numbered containers), describing any steps taken to conceal the sequence until interventions were assigned
- LABEL_10: Method used to generate the random allocation sequence
- LABEL_11: Methods for additional analyses, such as subgroup analyses and adjusted analyses
- LABEL_12: Settings and locations where the data were collected
- LABEL_13: Statistical methods used to compare groups for primary and secondary outcomes
- LABEL_14: The interventions for each group with sufficient details to allow replication, including how and when they were actually administered
- LABEL_15: Type of randomisation; details of any restriction (such as blocking and block size)
- LABEL_16: When applicable, explanation of any interim analyses and stopping guidelines
- LABEL_17: Who generated the random allocation sequence, who enrolled participants, and who assigned participants to interventions
Metrics
- accuracy: 0.731
- precision: 0.68
- recall: 0.73
- f1: 0.69
