# scibert_finetune_rct_consort 

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

This is a scibert finetuned model on 50 randomized controlled trial articles annotated with fine-grained [CONSORT checklist items](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398298/) at the sentence level.

### Model Description

<!-- Provide a longer summary of what this model is. -->

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



{"license": "apache-2.0", "library_name": "transformers", "datasets": ["apoorvasrinivasan/consort-tm"], "metrics": [{"accuracy": 0.731}, {"precision": 0.68}, {"recall": 0.73}, {"f1": 0.69}], "widget": [{"text": "this double-blind, randomised, parallel-group, placebo-controlled study consisted of a \u22642-week screening period, a 24-week treatment period and an 8-week post-treatment follow-up period."}, {"text": "the two primary end points were (1) the mean number of new dus per patient assessed by the investigator up to week 24 and (2) the time to healing of the cardinal ulcer up to week 24 in patients with cardinal ulcer healing maintained for 12 or more weeks."}]}
transformers pytorch safetensors bert text-classification dataset:apoorvasrinivasan/consort-tm license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #dataset-apoorvasrinivasan/consort-tm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

# 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



# 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
TAGS
#transformers #pytorch #safetensors #bert #text-classification #dataset-apoorvasrinivasan/consort-tm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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
TAGS
#transformers #pytorch #safetensors #bert #text-classification #dataset-apoorvasrinivasan/consort-tm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# 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