A Comprehensive Analysis of Robot-Assisted Surgery Uptake in the Pediatric Surgical Discipline
Bibliographic record
Abstract
Introduction: Robotic assisted surgery (RAS) is one of the most recent surgical approaches that has quickly been adopted. Over the last decade, a vast amount of manuscripts has been published. The quality of published literature about this innovative technology remains supported by case-reports and retrospective case-series. Historical behaviour of literature productivity and implementation of laparoscopy followed a similar trend. We present a historical bibliometric comparison of the most cited manuscripts since laparoscopy and RAS were implemented. Materials and Methods: A systematic search and review of the literature was undertaken by the authors. Literature search was performed in OVID, PubMed, EMBASE, Scopus, Web of Science and Google Scholar. The search period included all publications between 1985 and June 2018. All languages were included. Data analysis for graphical representation was performed using VOSviewer version 1.6.8 and Impact Index Analysis was used to adjust the citations by the time since publication. Results: A total of 1014 titles were identified. After applying exclusion criteria, 200 papers were included for the RAS arm and 402 for the laparoscopic one. Case-series was the most common type of publication. Average citations for laparoscopic manuscripts was 23 (SD +/- 31) and for RAS was 20 (SD +/- 31.5). The impact index analysis showed an average of 95 (SD +/-167) for laparoscopic manuscripts vs 66 (SD 101+/-) for RAS. The laparoscopic manuscript with the highest citation count had 199 citations with an impact index of 12.1. And the RAS manuscript with the highest citation count had 280 citations and an impact index of 4.3. Conclusion: Literature productivity in pediatric laparoscopic and RAS has quickly grown. Level of evidence literature productivity has been similar for both technologies with more impact for RAS in the community, exponentially growing at a faster pace than how laparoscopy was sin introduction. Current graduating generations have had a significant exposure to RAS during their adult training and for this reason we believe RAS has remained a leading topic in the pediatric urology specialty. Future directives need to focus on increasing the level of evidence to support innovation and development of pediatric instruments.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.035 | 0.048 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".