Unsuspected Pregnancies in Hysterosalpingography: Implementation and Review of a Multi-Institutional Pre-Procedural Pregnancy Screening Protocol
Bibliographic record
Abstract
PURPOSE: Owing to the increasing average age of first-time mothers, as well as advances in assistive reproductive technology, the number of hysterosalpingography (HSG) requests has continued to rise. This increases the likelihood of patients presenting with unsuspected early pregnancies prior to HSG. Currently, there is no standard of practice for the pre-procedural screening of pregnancy prior to HSG, with most institutions using patient-reported pregnancy status and unreliable menstrual cycle dating methods. We implemented a multi-institutional pre-procedural pregnancy screening protocol in order to determine the rate of unsuspected pregnancies prior to HSG and improve the quality and safety of these procedures. METHODS: Following multi-institutional and multidisciplinary input, a consensus protocol was formulated and implemented across 9 institutions in the Lower Mainland of British Columbia, Canada. Subsequent tracking of pregnancy testing was then performed over a period of 3 years. RESULTS: Pre-implementation review of protocols demonstrated large disparities between institutions. A total of 6333 HSG examinations were scheduled in the review period following implementation. Of these, 10 patients were found to have positive pregnancy tests (0.16%), despite self-reporting that they were not pregnant or had recent menstrual bleeding. DISCUSSION: Hysterosalpingography is contraindicated in pregnancy, yet we identified 10 unsuspected pregnancies in patients who would have otherwise undergone HSG examinations with existing guidelines. While there remains insufficient data on the deleterious effects of performing HSG on an unsuspected pregnancy, the potential physical, economical, and psychosocial consequences of performing an HSG during pregnancy are sufficient to merit consideration of relatively inexpensive routine pregnancy screening prior to HSG.
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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 | 0.226 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".