Subcutaneous Endoscopic-Assisted Ligation of the Internal Ring for Inguinal Hernia Repair in Neonates Under Spinal Anesthesia
Bibliographic record
Abstract
Introduction: While laparoscopy is now widely accepted for inguinal hernia repair in infants, it traditionally has required general anesthesia. We sought to evaluate the safety of laparoscopic inguinal hernia repair in infants under spinal anesthesia. Materials and Methods: We performed a retrospective cohort study of all inguinal hernia repairs at a single institution between December 2011 and June 2019 in patients younger than 6 months of age. Four groups were compared: laparoscopic under general anesthesia, laparoscopic with spinal anesthesia, open with spinal anesthesia, and open under general anesthesia. Main outcome measures include operative time, cost, and postoperative outcomes. These were assessed using Kruskal-Wallis median comparison. Results: Of the 226 patients meeting inclusion criteria, 54% (122/226) of patients underwent general anesthesia, while 46% (104/226) had spinal. When compared to general anesthesia, spinal anesthesia was associated with significantly shorter procedure times ( P < .01) and lower cost ( P < .01) for both open and laparoscopic approaches. Complications were few and underpowered to calculate significance across each group. Conclusions: Laparoscopic inguinal hernia repair can be safely performed in infants under spinal anesthesia without significant compromise of early perioperative outcomes. Advantages may include shorter procedure time and lower cost.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".