Outpatient Hysteroscopy with Combined Local Intracervical and Intrauterine Anesthesia
Bibliographic record
Abstract
BACKGROUND/AIMS: To evaluate the degree of pain during and after office hysteroscopy with combined intracervical and intrauterine anesthesia compared to intracervical anesthesia only. METHODS: We evaluated the amount of pain experienced during office hysteroscopy using combined local intracervical and intrauterine anesthesia, 10, 30, and 60 min after, and during endometrial biopsy prospectively in 37 infertile women (study group). We used a visual analog scale ranging from 0 to 10. Seventy-six women who received only intracervical anesthesia served as historical controls. RESULTS: The mean ages of patients in the control and study groups were comparable. Patients' perception of pain was significantly higher during endometrial biopsy than during or after hysteroscopy in the study patients (p < 0.01, 95% CI 0-3). The mean pain score in the control group was significantly higher than that in the study group during hysteroscopy (3.3 +/- 0.2 vs. 2.2 +/- 0.3; p < 0.05, 95% CI 0-2). However, there was no significant difference in the pain scores between the control and study groups during endometrial biopsy and 10, 30, and 60 min after the procedure. CONCLUSION: Endometrial biopsy is associated with more pain than office hysteroscopy. Additional intrauterine anesthesia with 1% lidocaine significantly reduces pain sensation during office hysteroscopy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".