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---
annotations_creators:
- synthetic
language:
- en
license: apache-2.0
multilinguality:
- monolingual
pretty_name: Myelography Imaging
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- named-entity-recognition
- news-articles-summarization
---
# Myelography Imaging
## Dataset Description
This dataset consists of **750 synthetic myelography examination records** representing a wide spectrum of spinal pathologies and patient experiences. Each record includes:
- **Patient demographics**: Age and sex.
- **Clinical symptoms prompting the procedure**: Detailed and verbose descriptions.
- **Procedural details**: Contrast medium type, injection site, and imaging modality used.
- **Verbose findings**: Observations such as spinal cord compression, herniated discs, tumors, and spinal stenosis.
- **Complications encountered**: Any issues arising during or after the procedure.
- **Follow-up recommendations**: Suggested next steps, including surgical consultation, physical therapy, or additional imaging.
### Example Data
| Age | Sex | Clinical Symptoms | Contrast Medium Type | Injection Site | Imaging Modality | Findings | Complications | Follow-up Recommendations |
|-----|-------|---------------------------------------------------------|----------------------|----------------|------------------|-------------------------------------------------|------------------------------------------|--------------------------------------------------|
| 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 |
| 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 |
## Intended Use
This dataset is intended for educational, research, and development purposes, including:
- Training and benchmarking in **natural language processing** (NLP) tasks.
- Developing tools for medical image analysis and clinical decision support systems.
- Conducting exploratory data analysis in synthetic medical datasets.
## Limitations
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.
## License
This dataset is distributed under the **Apache 2.0 License**.
## Citation
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### Acknowledgments
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