<scp>FIGO</scp> ‐ <scp>GCH</scp> joint consensus statement on the current status and recommendations for the use of blind intrauterine procedures in the evaluation and management of women with suspected intrauterine pathologies
Bibliographic record
Abstract
Historically, blind intrauterine procedures such as dilation and curettage (D&C) and blind endometrial biopsies have been the primary approach for diagnosing and managing intrauterine pathologies. However, these techniques lack direct visualization, leading to diagnostic limitations, incomplete treatment, and increased complication rates. Despite substantial advances in hysteroscopic technology, including high-definition imaging and minimally invasive instruments, blind procedures remain widely used. This paper examines the limitations of blind intrauterine procedures, underscoring the advantages of hysteroscopy, which provides real-time visualization and allows for more accurate, targeted interventions. With the adoption of the "See and Treat" philosophy, hysteroscopy enables nearly 90% of procedures to be performed in an office setting, enhancing both patient convenience and outcomes. FIGO and GCH advocate for the gradual replacement of blind procedures with hysteroscopic approaches whenever feasible, noting that hysteroscopy improves diagnostic accuracy, reduces risks, and minimizes the need for repeat interventions. Recommendations include expanding access to hysteroscopy through targeted training, especially in low- and middle-income countries, where financial and logistical barriers limit access to advanced gynecological care. Furthermore, this paper emphasizes the importance of patient-centered care, encouraging transparent counseling to support informed decision-making.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".