Paranasal Sinus Teratocarcinosarcoma with Intradural Extension
Bibliographic record
Abstract
Teratocarcinosarcoma, although a rare neoplastic entity, should be considered as a differential diagnosis in any middle-aged adult presenting with a history of intermittent unilateral epistaxis and nasal obstruction. Tissue biopsy may fail to reveal a full spectrum of histologic heterogeneity in these tumours, and definitive diagnosis is usually made with tumour resection. Aggressive treatment including surgery followed by adjuvant radiation therapy is advocated and confers a better rate of survival than radiotherapy alone. Our current report is unique in two respects. First, disease recurrence is usually manifested very early on, leading some authors to conclude that a neoplastic-free interval of 3 years or longer probably indicates a good chance of being cured. Our patient, in contrast, experienced a disease-free interval of 4 years before evidence of recurrence emerged. Second, intracranial extension with brain parenchymal involvement has not been previously reported despite the tumour's proximity to the anterior cranial fossa and its locally aggressive behaviour with frequent bony invasion. Despite intracranial invasion, our patient experienced a long disease-free interval. As such, even advanced disease should be treated aggressively.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".