A history of the treatment of appendicitis in children: lessons learned
Bibliographic record
Abstract
During the latter half of the 19th century, surgeons increasingly reported performing appendicectomies. Fitz from Harvard, Groves from Canada and Tait from Britain all recorded successful removal of the appendix. McBurney described the point of maximal tenderness in classic appendicitis and also the muscle-splitting incision centred on this point. Priority is given to McArthur in describing the lateral muscle-splitting incision. The direction of the cutaneous incision was later modified by Elliott and Lanz. Incisions that healed well were essential to recovery. Appendicectomy became a 'fashionable' operation after the London surgeon, Treves, removed the appendix of King Edward VII. Through the 20th century, the mortality from appendicitis fell notably with the advent of sulphonamide and penicillin, improvements in fluid therapy and safer anaesthesia. By 1990, diagnostic delay was the main cause of death. Semm performed the first laparoscopic appendicectomy in 1990, roundly criticized at the time for what is now a routine procedure. We view contemporary debates on the indications for appendicectomy, the best approach and how to optimize recovery in the light of the history of this intriguing disease.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".