Interobserver Reliability of the Paris Classification for Superficial Gastrointestinal Tract Neoplasms: A Systematic Review
Bibliographic record
Abstract
Background and study aims: The Paris classification characterizes the morphology of superficial gastrointestinal tract neoplasms. This system has been shown to predict the risk of submucosal invasion in certain subtypes of lesions. There is limited data that assesses its agreement amongst endoscopists. We performed a systematic review to summarize the available literature on the interobserver reliability (IOR) of the Paris classification. Methods: We conducted a search through December 2020 for studies reporting IOR of the Paris classification. Studies were included if they quantitatively evaluated the IOR of the Paris classification with at least five participating endoscopists. Two authors independently screened studies and abstracted data using an a priori-designed data collection form. Evaluation of study quality and risk of bias was performed using an adapted version of the Guidelines for Reporting Reliability and Agreement Studies. Results: = 0.54) and substantial in one study that evaluated gastric neoplasms (κw = 0.65). An educational intervention was conducted by three studies with variable methodology and no significant change in IOR. Conclusions: IOR of the Paris classification is moderate for superficial colonic neoplasms. Further study is needed to determine the reliability of this system for superficial gastric lesions. Standardized training programs are required to investigate the impact of educational intervention on the Paris classification amongst endoscopists.
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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 | 0.042 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".