Update on Laparoscopic Surgery at the Georgetown Public Hospital Corporation
Bibliographic record
Abstract
Background: Laparoscopic surgery is a pioneering technique that has metamorphosed the field of surgery in the past and is now considered the recommended surgical approach for many procedures. 1 The last published data showed an average of eight cases per month. 5 The purpose of this study was to assess an increase or decrease in the number of Laparoscopic surgeries at GPHC and if there were more advanced cases as compared to the last published data. Methods: Data of the laparoscopic surgeries done at GPHC for the year 2018 was obtained from the records in the Main Operating theatre. Data collected will focus on the number and type of Laparoscopic surgeries. Results: An audit of GPHC operating register shows a total of 180 cases for 2018 representing an average of 15 cases per month. There was a significant difference in the first six months of 2018 (5 cases per month) versus the last six months of 2018 (25 cases per month). A total of 19 cases were converted. Most of the advanced cases were done in latter half of 2018 and included 27 diagnostic laparoscopy, 9 inguinal hernias, 2 AP resections, 2 Graham’s patch, 2 ventral hernias, 2 rectopexy and 1 Heller’s myotomy. Conclusion: The number of laparoscopic surgeries at GPHC has increased significantly especially in the latter half of 2018. This number has risen to three times the number in the last published data. While the majority of cases continued to be cholecystectomy and appendectomies, a greater variety of advanced cases were done in 2018 as compared to the last published data.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.034 |
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".