Development of a novel classification system for anatomical variants of the puboprostatic ligaments with expert validation
Bibliographic record
Abstract
INTRODUCTION: We propose a novel classification system with a validation study to help clinicians identify and typify commonly seen variants of the puboprostatic ligaments (PPL). METHODS: A preliminary dissection of 6 male cadavers and a prospective dataset of over 300 robotic-assisted laparoscopic radical prostatectomies (RARP) recorded on video were used to identify 4 distinct ligament types. Then the prospectively collected database of surgical videos was used to isolate images of the PPL from RARP. Over 300 surgical videos were reviewed and classified with 1 to 5 pictures saved for reference of the type of PPL. To validate the new classification system, we selected 5 independent, blinded expert robotic surgeons to classify 100 ligaments based on morphology into a 4-type system: parallel, V-shaped, inverted V-shape, and fused. One week later, a subset of 25 photographs was sent to the same experts and classified. Statistical analyses were performed to determine both the intra-rater and inter-rater reliability of the proposed system. RESULTS: Inverted V-shaped ligaments were noted most frequently (29.97%), parallel and V-shaped ligaments were found at 19.19% and 11.11%, respectively and fused ligaments were noted less frequently (6.06%). There was good intra-rater agreement (κ = 0.66) and inter-rater agreement (κ = 0.67) for the classification system. CONCLUSIONS: This classification system provided standardized descriptions of ligament variations that could be adopted universally to help clinicians categorize the variants. The system, validated by several blinded expert surgeons, demonstrated that surgeons were able to learn and correctly classify the variants. The system may be useful in helping to predict peri- and postoperative outcomes; however, this will require further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".