Grading of Total Mesorectal Excision Specimens: Assessment of Interrater Agreement
Bibliographic record
Abstract
BACKGROUND: Total mesorectal excision is the standard of care for patients with rectal cancer. Pathological evaluation of the quality of the total mesorectal excision specimen is an important prognostic factor that correlates with local recurrence, but is potentially subjective. OBJECTIVE: This study aimed to determine the degree of variation in grading, both between assessors and between fresh and formalin-fixed specimens. DESIGN: Raters included surgeons, pathologists, pathology residents, pathologists' assistants, and pathologists' assistant trainees. Specimens were assessed by up to 6 raters in the fresh state and by 2 raters postfixation. Four parameters were evaluated: mesorectal bulk, surface regularity, defects, and coning. Interrater agreement was measured using ordinal α-values. SETTING: The study was conducted at a single academic center. MAIN OUTCOME MEASURES: The primary outcome was agreement between individuals when grading total mesorectal excision specimens. RESULTS: A total of 37 total mesorectal excision specimens were assessed. Reliability between all raters for fresh specimens for mesorectal bulk, surface regularity, defects, coning, and overall grade were 0.85, 0.85, 0.92, 0.84, and 0.91. When compared with all raters, pathologists and residents had higher agreement and pathologists and surgeons had lower agreement. Ordinal α-values comparing pathologist and pathologist's assistant agreement for overall grade were similar pre- and postfixation (0.78 vs 0.80), but agreement for assessing defects decreased postfixation. Among pathologists' assistants, agreement was higher when grading specimens postfixation than when grading fresh specimens. LIMITATIONS: Assessment bias may have occurred because of the greater number of pathologists' assistants participating than the number of residents and pathologists. CONCLUSIONS: The results indicate good interrater agreement for the assessment of overall grade, with defects showing the best interrater agreement in fresh specimens. Although total mesorectal excision specimens may be consistently graded postfixation, the assessment of defects postfixation may be less reliable. This study highlights the need for additional knowledge-transfer activities to ensure consistency and accurate grading of total mesorectal excision specimens. See Video Abstract at http://links.lww.com/DCR/A497.
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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 | 0.081 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".