Conus Medullaris Teratoma with Utilization of Fiber Tractography: Case Report
Bibliographic record
Abstract
Objective Conus medullaris teratomas are very rare tumors. Traditional preoperative diagnosis depended on the findings from magnetic resonance imaging (MRI). Tractography is a novel technique that has recently been utilized to diagnose spinal cord lesions. This case report shows that fiber tractography has great potential in preoperative diagnosis and postoperative follow-up of teratomas of the conus medullaris. Methods A 50-year-old man with a conus medullaris teratoma underwent tractography with the aim of visualizing the tumor in relation to the white matter tracts. The patient underwent a T12-L2 laminectomy, and the lesion was resected. The histopathology diagnosis was of a mature teratoma. Study Design Case report. Results Diffusion tensor imaging (DTI) and tractography provide more details about the white matter tracts in relation to space-occupying lesions that may be more sensitive than conventional MRI and have recently been utilized in spinal cord lesions. Fiber tracking has the ability to visualize the integrity of the white matter tracts at the level of the conus medullaris in relation to the lesion. The tracts appeared to be displaced by the lesion at the conus medullaris. Tractography also showed no white matter tracts within the lesion. Such findings are consistent with the characteristics of a benign lesion. Exploiting tractography in this case was helpful in predicting the nature of the lesion preoperatively and in planning the surgical intervention. Conclusions Conus medullaris teratomas mostly affect adults. Patients generally present with a long history of clinical symptoms prior to diagnosis. Surgery is required for diagnosis, and the goal should be complete tumor excision without sacrificing any neurologic functions. The use of DTI and tractography, in addition to conventional MRI, has the potential to be very valuable for the diagnosis, surgical planning, and follow-up of patients with conus medullaris teratomas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".