Hysterosalpingography in the investigation of women requesting reversal of sterilization. Should it play a role?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the role of hysterosalpingography (HSG) in the investigation of women requesting reversal of sterilization (ROS). STUDY DESIGN: A prospective, cohort study at a university-affiliated, tertiary fertility clinic. All women proceeding to surgery were investigated with HSG in addition to other routine screening. Findings from HSG were tabulated to document the prevalence of abnormalities and correlated with histologic findings in resected tubal segments. RESULTS: One hundred sixteen women of 166 referred for ROS underwent HSG during the initial evaluation. HSG depicted abnormal tubal images in only 2 cases (1.7%) and abnormal uterine images in 15 (12.9%) cases. In the cases of abnormal tubal findings, there was no association with histologic findings. The specificity of HSG as a diagnostic screening tool was 90%; however, the small number of cases with abnormal histology prevented calculation of an accurate estimate of sensitivity of HSG as an investigative tool before ROS. A less invasive method of imaging the uterus, such as a vaginal ultrasound, may provide more valuable information in evaluating the future fertility outcome in these women. CONCLUSION: The prevalence of abnormalities of the proximal oviductal segment identified by HSG is too low to warrant the routine use of HSG as a diagnostic tool.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".