<scp>P11</scp>.16: Role of <scp>MRI</scp> in aid in patient's counselling and decision‐making in cases of cervical teratomas
Bibliographic record
Abstract
Cervical teratomas are rare congenital neoplasms that can be managed with special delivery procedures such as the EXIT (ex-utero intrapartum treatment) procedure. Nonetheless, they have a high mortality rate. Prenatal diagnosis is critical in selection and management of cases. Choice of the most appropriate imaging technique is unique for each patient. A 24-year-old woman, gravida 3, para 1, was referred for a cervical mass seen at her 20-week routine ultrasound scan. The mass was assessed by serial follow-up ultrasounds in our center and was believed to be a cervical teratoma. Rapid extension of the tumor and apparition of polyhydramnios then warranted further assessment of tongue and jaw anatomy and function. Fetal MRI and a bone reconstruction CT scan confirmed severe mandibular malformation and the tongue's inseparability from the tumor, thus providing crucial information on prognostic. After multidisciplinary counselling, the patient opted for neonatal palliative care instead of an EXIT procedure. Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".