Postoperative Recovery Time in Inguinal Herniotomy Under Ilioinguinal/Iliohypogastric Block and Sedation Versus General Anesthesia: A Retrospective Propensity-Score Matched Study
Bibliographic record
Abstract
Background Associated advantages of ilioinguinal/iliohypogastric block and sedation versus general anesthesia (GA) for inguinal hernia repair have not been reported. The use of regional anesthesia (RA) is advantageous during the COVID-19 pandemic as it eliminates the need for airway manipulation.This study aimed to determine the association between postoperative recovery time when ilioinguinal/iliohypogastric block and sedation were utilized for inguinal hernia versus GA. Method This single-center retrospective study used multivariable logistic regression to model the anesthetic modality as a function of age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) physical status, major comorbidities to generate a propensity score for each patient for matching. Results After screening 295 patients, 80 patients each in the general and regional anesthesia groups were matched.RA was associated with a 35.6 minutes (95% CI: -46.6 to -25.0) shorter total postoperative recovery time when compared to GA without the increased preoperative time and adverse outcomes. Conclusions Inguinal hernia repair, when performed under ilioinguinal/iliohypogastric block and sedation, was associated with reduced postoperative recovery time. This can be advantageous during the time of the COVID-19 pandemic to reduce the risk of aerosol generation and shorten hospital stay. Future research can focus on establishing a causal relationship.
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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".