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---
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annotations_creators:
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- synthetic
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language:
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- en
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license: apache-2.0
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multilinguality:
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- monolingual
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pretty_name: Myelography Imaging
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size_categories:
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- 1K<n<10K
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source_datasets:
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- original
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task_categories:
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- text-classification
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task_ids:
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- named-entity-recognition
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- news-articles-summarization
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---
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# Myelography Imaging
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## Dataset Description
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This dataset consists of **750 synthetic myelography examination records** representing a wide spectrum of spinal pathologies and patient experiences. Each record includes:
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- **Patient demographics**: Age and sex.
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- **Clinical symptoms prompting the procedure**: Detailed and verbose descriptions.
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- **Procedural details**: Contrast medium type, injection site, and imaging modality used.
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- **Verbose findings**: Observations such as spinal cord compression, herniated discs, tumors, and spinal stenosis.
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- **Complications encountered**: Any issues arising during or after the procedure.
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- **Follow-up recommendations**: Suggested next steps, including surgical consultation, physical therapy, or additional imaging.
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### Example Data
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| Age | Sex | Clinical Symptoms | Contrast Medium Type | Injection Site | Imaging Modality | Findings | Complications | Follow-up Recommendations |
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|-----|-------|---------------------------------------------------------|----------------------|----------------|------------------|-------------------------------------------------|------------------------------------------|--------------------------------------------------|
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| 45 | Male | Chronic lower back pain with radiating leg pain | Iodinated contrast | Lumbar spine | X-ray | Large herniated disc at L4-L5 | No complications | Referral to neurosurgery for evaluation |
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| 60 | Female| Acute onset lower limb weakness post-trauma | Gadolinium-based contrast| Cervical spine | MRI | Severe spinal cord compression | Localized discomfort at injection site | Follow-up imaging with enhanced MRI |
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## Intended Use
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This dataset is intended for educational, research, and development purposes, including:
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- Training and benchmarking in **natural language processing** (NLP) tasks.
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- Developing tools for medical image analysis and clinical decision support systems.
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- Conducting exploratory data analysis in synthetic medical datasets.
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## Limitations
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This dataset is entirely synthetic and does not contain real patient data. It should not be used for diagnostic purposes. The findings and follow-up recommendations are simulated and may not encompass the full complexity of real-world scenarios.
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## License
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This dataset is distributed under the **Apache 2.0 License**.
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## Citation
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---
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### Acknowledgments
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