Training programme in gasless laparoscopy for rural surgeons of India (TARGET study) - Observational feasibility study
Bibliographic record
Abstract
BACKGROUND: Benefits of laparoscopic surgery are well recognised but uptake in rural settings of low- and middle-income countries is limited due to implementation barriers. Gasless laparoscopy has been proposed as an alternative but requires a trained rural surgical workforce to upscale. This study evaluates a feasibility of implementing a structured laparoscopic training programme for rural surgeons of North-East India. METHODS: A 3-day training programme was held at Kolkata Medical College in March 2019. Laparoscopic knowledge and Fundamentals of Laparoscopic Skills (FLS) were assessed pre and post simulation training using multiple choice questions and the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS), respectively. Competency with an abdominal lift device was assessed using the Objective Structured Assessment of Technical Skills (OSATS) and live operating performance via the Global Operative Assessment of Laparoscopic Skills (GOALS) scores during live surgery. Costs of the training programme and qualitative feedback were evaluated. RESULTS: Seven rural surgeons participated. There was an improvement in knowledge acquisition (mean difference in MCQ score 5.57 (SD = 4.47)). The overall normalised mean MISTELS score for the FLS tasks improved from 386.02 (SD 110.52) pre-to 524.40 (SD 94.98) post-training (p = 0.09). Mean OSATS score was 22.4 out of 35 (SD 3.31) indicating competency with the abdominal lift device whilst a mean GOALS score of 16.42 out of 25 (SD 2.07) indicates proficiency in performing diagnostic laparoscopy using the gasless technique during live operating. Costs of the course were estimated at 354 USD for trainees and 461 USD for trainers. CONCLUSION: Structured training programme in gasless laparoscopy improves overall knowledge and skills acquisition in laparoscopic surgery for rural surgeons of North-East India. It is feasible to deliver a training programme in gasless laparoscopy for rural surgeons. Larger studies are needed to assess the benefits for wider adoption in a similar context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".