Advances in imaging modalities for spinal tumors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The spinal cord occupies a narrow region and is tightly surrounded by osseous and ligamentous structures; spinal tumors can damage this structure and deprive patients of their ability to independently perform activities of daily living. Hence, imaging is vital for the prompt detection and accurate diagnosis of spinal tumors, as well as determining the optimal treatment and follow-up plan. However, many clinicians may not be familiar with the imaging characteristics of spinal tumors due to their rarity. In addition, spinal surgeons might not fully utilize imaging for the surgical planning and management of spinal tumors because of the complex heterogeneity of these lesions. In the present review, we focus on conventional and advanced spinal tumor imaging techniques. These imaging modalities include computed tomography, positron emission tomography, digital subtraction angiography, conventional and microstructural magnetic resonance imaging, and high-resolution ultrasound. We discuss the advantages and disadvantages of conventional and emerging imaging modalities, followed by an examination of cutting-edge medical technology to complement current needs in the field of spinal tumors. Moreover, machine learning and artificial intelligence are anticipated to impact the application of spinal imaging techniques. Through this review, we discuss the importance of conventional and advanced spinal tumor imaging, and the opportunity to combine advanced technologies with conventional modalities to better manage patients with these lesions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it