A Systemic Review on Single-Port and Multiport Laparoscopic Hernioplasty
Bibliographic record
Abstract
Herniorrhaphy or Hernioplasty is the surgical treatment for hernia. This surgical procedure is mainly done using local or general anesthesia with laparoscope or conventional incision. Laparoscopic hernioplasty is the best suited laparoscopic technique for almost all the abdominal hernias. This technique has gained its approval in recent treatment and is being widely used. Despite the fact that it is for the most part of safe operation, postoperative complications are found to be less. The recovery time after the surgery is found to be 1 to 2 weeks. Single port laparoscopy is the marginally invasive surgical process where the surgery takes place by a single entry point mainly the umbilicus. This single port technique leaves only a single scar. Multiport laparoscopic technique is the traditional technique where it uses many entry points for operation. Thus the single port laparoscopic technique consists of many advantages like faster recovery time, less blood loss, less post-operative pain etc. This study systematically reviewed the existing literatures for comparing the single site over the multiport hernia repair. The outcomes like hospital stay, operative time, complications and blood loss are reviewed. The remedial advantages in the general management of hernia are reviewed in detail. This review concludes that the single port laparoscopic hernioplasty is the most advantaged technique than the multiport.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".