The Blooming phenomenon: a rarity, but a dilemma in hysteroscopic resection of myomas
Bibliographic record
Abstract
Modern surgical technologies allow gynecologists to treat most submucosal myomas hysteroscopically by some form of resection. What appears on imaging or direct visualization to be a submucosal myoma can be a single tumor, or may represent multiple smaller myomas appearing as one, compacted together in a typical pseudo capsule. During myoma resection, the effect of the media used to induce distension can vary, depending on the morphology of the myomas. After starting resection, the pressure of the distending media can push truly solitary myomas to somewhat flatten against the uterine wall. However, in the second type of myoma, the fluid can displace the myomas into the uterine cavity, an appearance similar to the blooming of a flower. The tip of the hysteroscope may enter the dissected spaces between the myomas, which impairs the panoramic view. This phenomenon may cause inadequate treatment of the myomas encountered during hysteroscopic myomectomy. In this study, the “Blooming phenomenon” is introduced, and the problems created by this phenomenon and solutions for its management are considered.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".