P18 Assessment of da Vinci robotic system for paediatric laparoscopic procedures
Bibliographic record
Abstract
Abstract Introduction We aimed to evaluate the da Vinci surgical system in its application for robotic-assisted laparoscopic surgery in paediatric patients. Methods A narrative review of the literature on the use of this technology was performed following ENTREQ guidelines using PubMed/Medline, EMBASE, and Google Scholar databases with no setting or language restrictions. Results A total of 16 publications were selected for inclusion. Although the literature on the accuracy and precision of this technology are encouraging, its use in paediatric patients are still in its early stages and has yet to be explored in great detail. In addition to the technical intricacies, training and learning curve, port placement complications, cost, we identified concerning shortcomings including its bulkiness and lack of force feedback, which lead to procedural injuries notably, tearing of muscles, blood vessels, and nerves, as well as surgical error. For both, we propose sensory haptic feedback systems, soft tissue model, image-guided or virtual reality simulation training to reduce these injury-related complications. However, this does not replace the necessity for “supervised trial and error” operation of the robotic system in surgical settings. Conclusion Innovations in educational training for robotic surgery include tele-presence surgeries and robotic tele- mentoring, whereby expert surgeons share the same surgical field of view and controls as the training surgeon. In spite of this, there are inevitable risks associated with training when training surgeons must practice through trial and error on real patients and an emphasis must be placed on apposite pre-procedural surgical training. Take-home message Though promising, the use of the Da Vinci robotic system in children is still emerging and thus warrants further evaluation, training, and development prior to its routine implementation for use.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".