Endolymphatic Sac Tumours: Surgical Management
Bibliographic record
Abstract
BACKGROUND: Endolymphatic sac tumours (ELSTs) have been known as an individual tumour entity only since 1984. ELSTs may occur either solitarily and sporadically or as a hereditary manifestation associated with von Hippel-Lindau (VHL) disease. The latter association was first observed in 1992 and confirmed by molecular genetic analysis of the VHL gene. No consensual diagnostic and treatment strategy of ELST exists at present. METHODS: Based on imaging criteria in computed tomography, magnetic resonance imaging (MRI), and magnetic resonance angiography, we developed a staging system to classify ELST in a series of seven consecutive patients in an attempt to custom-tailor the surgical approach. Type A referred to tumours that were locally confined without temporal bone erosion or infiltration of the dura (n = 2); type B tumours showed evidence of bone infiltration of the osseous labyrinth and sensorineural hearing loss (n = 2); and in type C, the tumour further invaded the sigmoid sinus and jugular bulb (n = 3). Two patients suffered from VHL disease. RESULTS: In all patients, the tumour was completely removed. Stage-adapted surgical approaches included various transpetrosal procedures, from the translabyrinthine to the infratemporal approaches. The functional integrity of the facial nerve was maintained in all tumour stages, whereas the vestibulocochlear nerve could be preserved only in patients with type A tumours. Follow-up MRI demonstrated no local tumour recurrence during a postoperative observation period ranging from 4 to 38 months. CONCLUSION: Stage-based surgical strategy enables the complete removal of ELST with minor morbidity. Transmastoid approaches are most efficient for resection of the tumour matrix to prevent local recurrence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".