Does type of anesthesia during procedural management of suspected renal colic during pregnancy have an impact on preterm birth?
Bibliographic record
Abstract
INTRODUCTION: Anesthesia choice during the procedural management of suspected renal colic during pregnancy may vary based on available resources and patient or provider preferences, as there are no specific recommendations. Our objective was to evaluate whether preterm birth (<37 weeks) was associated with anesthesia type, anesthesia timing by trimester, or procedure type. METHODS: We retrospectively identified pregnant patients who required procedural management with ureteral stent, percutaneous nephrostomy (PCN), or ureteroscopy (URS) for suspected renal colic based on laboratory and imaging findings from 2009-2021 at our center. Analyzed data included anesthesia type (local analgesia only, monitored anesthesia care [MAC], spinal anesthesia, or general anesthesia), trimester of procedure, procedure type, and obstetric outcomes, including preterm birth. RESULTS: The study cohort included 96 patients who underwent 231 total procedures, including primary URS, PCN, and stent, as well as PCN and stent change. The median gestational age was 38.7 weeks (37.1-39.5), and preterm birth rate was 15.8%. The most common anesthetic used across all procedures and trimesters was MAC. PCN was associated with the use of less invasive analgesia or anesthesia, whereas endoscopic procedures were more commonly performed with spinal or general anesthesia. Using multivariable logistic regression, procedure type was associated with preterm birth, but not anesthesia type or timing by trimester. CONCLUSIONS: Anesthesia type and timing were not associated with preterm birth, and selection may be influenced by resources, clinical scenario, or patient and provider preferences.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".