The Impact of Lymphovascular Space Invasion on Recurrence and Survival in Iranian Patients With Early Stage Endometrial Cancer
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess the impact of lymphovascular space involvement (LVSI) on recurrence and survival in early stage of endometrial cancer (EC). METHODS: Patients with EC referred to Imam Khomeini Hospital in Tehran were examined and enrolled over a 10-year period (2004 - 2015). The effect of LVSI on recurrence and overall survival was analyzed using the Kaplan-Meier and log-rank test methods. RESULTS: A total of 160 patients with early stage EC were identified. Out of 160 women with EC, 135 (84.4%) underwent primary surgery. One hundred and twenty-one (76.2%) patients were not found to have LVSI, whereas 38 (23.8%) were found to have LVSI. Of the 38 patients with LVSI, 21 (55.3%) had endometrioid cell type tumor, 10 (26.3%) had serous, one (2.6%) had clear cell and six (15.8%) had adeno-squamous cell type tumor. CONCLUSION: The presence of LVSI represents a factor strongly associated with high risk of recurrence and poor survival in early stage EC. Patients with lower International Federation of Obstetrics and Gynecology (FIGO) stages may be at increased risk of recurrence and a poor overall survival if the pathological findings confirm the presence of LVSI. Thus, LVSI should be added to the traditional factors used to decide whether patients with early stage EC are at high risk of recurrence and adjuvant therapy planning.
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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 | 0.002 |
| 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) | 0.001 | 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".