The Learning Curve for Robotic Lateral Pelvic Lymph Node Dissection for Rectal Cancer: A View From the West
Bibliographic record
Abstract
BACKGROUND: Lateral pelvic lymph node dissection is performed for selected patients with rectal cancer with persistent lateral nodal disease after neoadjuvant therapy. This technique has been slow to be adopted in the West because of concerns regarding technical difficulty. This is the first report on the learning curve for lateral pelvic lymph node dissection in the United States or Europe. OBJECTIVE: This study aimed to analyze the learning curve associated with robotic lateral pelvic lymph node dissection. DESIGN: Retrospective observational cohort. SETTING: Tertiary academic cancer center. PATIENTS: Consecutive patients from 2012 to 2021. INTERVENTION: All patients underwent robotic lateral pelvic lymph node dissection. MAIN OUTCOME MEASURES: The primary end points were the learning curves for the maximum number of nodes retrieved and urinary retention, which was evaluated with simple cumulative sum and 2-sided Bernoulli cumulative sum charts. RESULTS: Fifty-four procedures were included. A single-surgeon learning curve (n = 35) and an institutional learning curve are presented in the analysis. In the single-surgeon learning curve, a turning point marking the end of a learning phase was detected at the 12th procedure for the number of retrieved nodes and at the 20th procedure for urinary retention. In the institutional learning curve analysis, 2 turning points were identified at the 13th procedure, indicating progressive improvements for the number of retrieved nodes, and at the 27th procedure for urinary retention. No sustained alarm signals were detected at any time point. LIMITATIONS: The retrospective nature, small sample size, and the referral center nature of the reporting institution may limit generalizability. CONCLUSIONS: In a setting of institutional experience with robotic colorectal surgery, including beyond total mesorectal excision resections, the learning curve for robotic lateral pelvic lymph node dissection is acceptably short. Our results demonstrate the feasibility of the acquisition of this technique in a controlled setting, with sufficient case volume and proctoring to optimize the learning curve. See Video Abstract. LA CURVA DE APRENDIZAJE DE LA DISECCIN ROBTICA DE LOS GANGLIOS LINFTICOS PLVICOS LATERALES EN EL CNCER DE RECTO UNA VISIN DESDE OCCIDENTE: ANTECEDENTES:La disección lateral de los ganglios linfáticos pélvicos se realiza en pacientes seleccionados con cáncer de recto con enfermedad ganglionar lateral persistente tras el tratamiento neoadyuvante. La adopción de esta técnica en Occidente ha sido lenta debido a la preocupación por su dificultad técnica. Éste es el primer informe sobre la curva de aprendizaje de la disección de los ganglios linfáticos pélvicos laterales en EE.UU. o Europa.OBJETIVO:El objetivo de este estudio fue analizar la curva de aprendizaje asociada a la disección robótica de los ganglios linfáticos pélvicos laterales.DISEÑO:Cohorte observacional retrospectiva.LUGAR:Centro oncológico académico terciario.PACIENTES:Pacientes consecutivos desde 2012 al 2021.INTERVENCIÓN:Todos los pacientes fueron sometieron a disección robótica de ganglios linfáticos pélvicos laterales.PRINCIPALES MEDIDAS DE RESULTADO:Los criterios de valoración primarios fueron las curvas de aprendizaje tomando en cuenta el mayor número de ganglios recuperados y la retención urinaria que fueron evaluados con gráficos de suma acumulativa simple y de suma acumulativa de Bernoulli de dos caras.RESULTADOS:Fueron incluidos 54 procedimientos. En el análisis se presentan una curva de aprendizaje de un solo cirujano (n = 35) y una curva de aprendizaje institucional. En la curva de aprendizaje de un solo cirujano, se detectó un punto de inflexión que marcaba el final de una fase de aprendizaje en el duodécimo procedimiento para el número de ganglios extraídos y en el vigésimo para la retención urinaria. En el análisis de la curva de aprendizaje institucional, se identificaron dos puntos de inflexión en las intervenciones 13.ª y 26.ª, que indicaron mejoras progresivas en el número de ganglios extraídos, y en la 27.ª en la retención urinaria. No se detectaron señales de alarma sostenidas en ningún momento.LIMITACIONES:La naturaleza retrospectiva, el pequeño tamaño de la muestra y la naturaleza de centro de referencia de la institución informante que pueden limitar la capacidad de generalizarse.CONCLUSIONES:En un entorno de experiencia institucional con cirugía robótica colorrectal incluyendo más allá de las resecciones TME, la curva de aprendizaje para la disección robótica de ganglios linfáticos pélvicos laterales es aceptablemente corta. Nuestros resultados demuestran la viabilidad de la adquisición de esta técnica en un entorno controlado, con un volumen de casos suficiente y una supervisión que puede optimizar la curva de aprendizaje. (Traducción-Dr. Osvaldo Gauto ).
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".