Adenocarcinoma involving the uterine cervix: magnetic resonance imaging findings in tumours of endometrial, compared with cervical, origin.
Bibliographic record
Abstract
PURPOSE: To determine the distinctive magnetic resonance imaging (MRI) features of cervical and endometrial adenocarcinoma that present clinically as a cervical mass. MATERIALS AND METHODS: From 1999 to 2002, 56 patients with adenocarcinoma on the initial biopsy of a cervical mass underwent MRI at our institution. Of these, 42 had a visible mass on MRI. Pathology review of all available tissue was the reference standard. A site of origin was determined by the pathologist in 38 of the 42 patients, and these were the cases evaluated; of these patients, 32 cases had adenocarcinoma and 6 had adenosquamous cancers. RESULTS: Findings were significantly more prevalent in patients with adenocarcinomas of endometrial, compared with cervical, origin for endometrial thickening (11 [73%] and 3 [13%], respectively; P = 0.0003), endometrial mass (11 [73%] and 1 [4%], respectively; P < 0.0001), endometrial cavity expansion by a mass (9 [60%] and 2 [9%], respectively; P = 0.001), and invasion of myometrium from endometrium (9 [60%] and 0, respectively; P < 0.0001). CONCLUSION: Adenocarcinomas of the endometrium that involve the cervix have MRI features that help distinguish them from primary adenocarcinomas of the cervix.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".