Patient Understanding of Uterine Fibroids and the Different Surgical Approaches to Hysterectomy
Bibliographic record
Abstract
Objective: The purpose of this study was to assess understanding of the hysterectomy procedure and uterine fibroids among women in a general gynecology clinic. Materials and Methods: This was an anonymous cross-sectional survey. We adapted and pilot tested a survey instrument designed to assess understanding of the hysterectomy procedure and of uterine fibroids. The final version of the survey consisted of basic demographic questions, followed by 28 knowledge questions (Canadian Task Force Classification II-2). The survey was disseminated to women in the waiting room of one of our gynecology clinics. The patient population included women 18 years and older. Results: The mean age of respondents was 33.5 years old. In total, 69.5% of the respondents had at least some college education. In the group of questions related to different types of hysterectomies, the most poorly answered question was “Which type of hysterectomy has the highest risk of damage to the bladder?” Less than 40% of the respondents were able to identify a laparoscopic and robotic hysterectomy based on a written description. Of questions about uterine fibroids, the most poorly answered question was whether cancer that looks like fibroids is common, with >90% of the respondents incorrectly thinking that cancer that resembled fibroids is common. More than half of respondents did not know what a fibroid is. Conclusions: In this analysis of the understanding of the hysterectomy procedure and fibroids among an educated population, overall understanding was poor. Specific areas where knowledge was particularly poor were the different ways of doing a hysterectomy and uterine fibroids.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".