The efficiency of right hemicolectomy specimen grossing and blocking: a quality assurance study
Bibliographic record
Abstract
Pathologists’ Assistants (PAs) are increasingly utilized in the pathology laboratory to perform grossing duties. With this increase in physician extenders performing gross dissections, steps should be taken to standardize grossing guidelines so that Pathologists are always given adequate and informative tissue sections. This study aims to analyze the impact of the introduction of a grossing manual on the grossing quality for malignant right hemicolectomy specimens in Manitoba, Canada. Five aspects of colorectal cancer grossing were analyzed in 88 pre-manual specimens and 92 post-manual specimens, including: total number of blocks submitted to histology, number of blocks containing tumour submitted, number of blocks containing margin submitted, lymph node submission, and the submission of non-informative tissue sections. After introduction of the manual, lymph node submission utilized significantly less blocks, and the submission of multiple types of non-informative tissue blocks (such as the sampling of anastomotic donuts when the margin is well-clear) significantly decreased. Additionally, the grossing manual recommends no more than five blocks of tissue including tumour should be submitted, however the submission of six or more tumour sections significantly increased and the total number of blocks submitted per case did not change after the manual’s introduction. Based on this study’s findings, two recommendations are made for future versions of the grossing manual: (1) sections of proximal and distal margin may not need to be submitted if the tumour is greater than 2cm from margin, and the tumour is not diffusely infiltrative or associated with inflammatory bowel disease; and (2) PAs should consult with a Senior PA or Pathologist when submitting six or more blocks. These recommendations will likely improve blocking efficiency and decrease costs associated with the processing of blocks, while providing patients with the same quality of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".