Medical management prior to hysterectomy for benign indications: Trends from a tertiary-care centre
Bibliographic record
Abstract
Objective: Women historically had a hysterectomy for benign gynaecological indications (fibroids, abnormal uterine bleeding, pelvic organ prolapse and chronic pelvic pain).Multiple effective medical therapies now exist for all of these indications, with variable use.As various provinces in Canada move towards a quality-based model of funding, this study aimed to understand trends in usage of these medical therapies prior to proceeding to hysterectomy to identify potential areas for quality improvement.Methods: A retrospective chart review was conducted of all hysterectomies performed for benign indications over a 6-month period at a tertiary care institution focusing on three major areas: medical management prior to proceeding to hysterectomy (how many therapies tried and for how long), pre-operative optimization once hysterectomy was decided and surgical approach (vaginal, laparoscopic or abdominal).Results: Thirteen percent of women did not have documentation of receiving any counseling about alternative medical therapies.When women were counseled regarding one or more medical therapies, 30% declined to try any of the options and 57% tried at least one.Only 19.9% of women tried more than one form of medical management prior to proceeding to hysterectomy. Conclusion:Our study indicates that medical management is not being adequately discussed, trialed, and documented in women undergoing hysterectomy for benign indications
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".