A safer, simpler, classic intrafascial supracervical hysterectomy technique.
Bibliographic record
Abstract
OBJECTIVES: Our aim is to introduce the technical aspects and advantages of a new classic intrafascial supracervical hysterectomy (CISH) technique over the conventional technique. METHODS: We performed a retrospective evaluation (Canadian Task Force classification II-2) of 200 women who underwent conventional CISH technique (100 cases), between March 2000 and September 2000, or the new CISH technique (100 cases) between May 2002 and November 2002. The charts of these 200 women were reviewed regarding patient characteristics, indications, uterine weight, estimated blood loss, operating time, and hemoglobin change. RESULTS: The women who underwent the new CISH had significantly shorter operating time as compared with operating time for the conventional method. Although no significant difference existed in the estimated blood loss, the hemoglobin change, which is an objective sign of blood loss, was significantly smaller using the new CISH technique than using the conventional CISH technique. CONCLUSIONS: The new CISH technique is safer, more convenient, faster, and results in less blood loss than the conventional technique, especially when the uterus is markedly enlarged by a large myoma, the ovarian ligament is too short, or the ovary and uterus are very closely adherent.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".