Good practice with fluid management in operative hysteroscopy
Bibliographic record
Abstract
Hysteroscopic surgery requires a balance of continuous controlled irrigation and aspiration to distend the endometrial cavity to a degree that provides the clear and stable visual environment necessary for diagnostic and therapeutic procedures. Whereas the preferred distending solution should be isotonic and isonatremic, radiofrequency (RF) electrosurgery with monopolar instrumentation can only be performed with non-ionic (hyponatremic) solutions. Absorption of as little as 500 mL and certainly more than 1000 mL of non-ionic solutions can result in fluid overload and/or dilutional hyponatremia with potentially serious adverse effects under certain conditions and patient characteristics. Both hysteroscopic RF electrosurgery with bipolar instrumentation and electro-mechanical morcellation and aspiration systems use isotonic and isonatremic solutions. Depending on the clinical context, absorption of more than 1500 mL of isonatremic solutions can also result in serious adverse effects. Automated fluid management systems are preferred and recommended, and surgeons should aim to maintain the maximum allowable intravasation of distending media below 1000 and 1500 mL for non-ionic and ionic fluids, respectively.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".