The Evolution of the Appendectomy: From Open to Laparoscopic to Single Incision
Bibliographic record
Abstract
Beginning with its initial description by Fitz in the 19th century, acute appendicitis has been a significant long-standing medical challenge; today it remains the most common gastrointestinal emergency in adults. Already in 1894, McBurney advocated for the surgical removal of the inflamed appendix and is credited with the initial description of an Open Appendectomy (OA). With the introduction of minimally invasive surgery, this classic approach evolved into a procedure with multiple, smaller incisions; a technique termed Laparoscopic Appendectomy (LA). There is much literature describing the advantages of this newer approach. To name a few, patients have significantly less wound infections, reduced pain, and a reduction in ileus compared with the OA. In the past few years, Single Incision Laparoscopic Appendectomy (SILA) has gained popularity as the next major evolutionary advancement in the removal of the appendix. Described as a pioneer in the era of "scarless surgery," it involves only one transumbilical incision. Patients are postulated to have reduced post-operative complications such as infection, hernias, and hematomas, as well as a quicker recovery time and less post-operative pain scores, in comparison to its predecessors. In this review, we explore the advancement of the appendectomy from open to laparoscopic to single incision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".