MR Imaging of Cervical Carcinoma: A Practical Staging Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cervical carcinoma is the third most common gynecologic malignancy and is typically seen in younger women, often with serious consequences. The International Federation of Gynecology and Obstetrics (FIGO) staging system provides worldwide epidemiologic and treatment response statistics. However, there are significant inaccuracies in the FIGO staging system, and magnetic resonance (MR) imaging, although not included in that system, is now widely accepted as optimal for evaluation of important prognostic factors such as lesion volume and metastatic lymph node involvement that will help determine the treatment strategy. MR imaging examination obviates the use of invasive procedures such as cystoscopy and proctoscopy, especially when there is no evidence of local extension. Brachytherapy and external beam therapy are optimized with MR imaging evaluation of the shape and direction of lesion growth. In general, T2-weighted MR imaging more clearly delineates cervical carcinoma and is preferred for evaluation of the lymph nodes. Dynamic gadolinium-enhanced T1-weighted imaging may help identify smaller tumors, detect or confirm invasion of adjacent organs, and identify fistulous tracts. MR imaging staging, when available, is invaluable for identifying important prognostic factors and optimizing treatment strategies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it