The diagnosis of spinal tumors: established and emerging methods
Bibliographic record
Abstract
INTRODUCTION: Spinal tumors comprise a biologically heterogeneous group of neoplasms associated with different treatment strategies and varying prognoses. The accurate diagnosis of these tumors is, therefore, imperative to direct appropriate patient care. Here, the authors review established and emerging tools in the fields of diagnostic imaging and pathology for the assessment of spinal tumors. AREAS COVERED: An approach to standard diagnostic imaging modalities such as plain radiographs, computed tomography, magnetic resonance imaging and angiography is discussed with emphasis on the application of emerging methods such as diffusion tensor imaging, and magnetic resonance spectroscopy to the diagnosis of spinal tumors. Tissue-based diagnostic approaches, including histology and immunohistochemistry, are also reviewed, with a discussion of future trends in the fields of genetics and molecular biology. The authors additionally summarize classical findings of common spinal tumors. EXPERT OPINION: With the appropriate clinical suspicion, numerous complementary tools are used to facilitate the diagnosis of spinal tumors. Increasing knowledge of tumor biology and better discrimination of tumor subtypes will continue to play a significant role in guiding patient-specific treatment decisions.
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.
How this classification was reachedexpand
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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".