A135 ASSESSING THE HISTOLOGICAL QUALITY OF ENDOSCOPIC BIOPSY SAMPLES OBTAINED USING NOVEL MULTIBITE FORCEPS FROM A PORCINE GASTRIC SPECIMEN
Bibliographic record
Abstract
Abstract Background Tissue sampling is often limited to acquisition of one to two biopsy samples during a single pass. The ability to obtain more than two biopsies during a single pass can improve diagnostic yield however is potentially limited by poor specimen quality and loss of specimens. The multibite forceps used in this study have a unique geometry with the ability to store up to six biopsy samples taken consecutively with easy removal of the samples when shaken in solution. If multiple biopsies can be taken during a single pass with preserved specimen quality then we can reduce procedure time, improve efficiency and sensitivity of biopsies. Aims To evaluate the histological quality of the first biopsy sample compared to the last (sixth) biopsy sample acquired consecutively with the multibite forcep during a single act. Methods A porcine stomach was coloured with surgical dye to create six separate segments. An experienced endoscopist used single use disposable MultiCROC multi-sampling biopsy forceps to acquire six consecutive biopsies. Biopsies were manually separated into the order of which they were acquired (biopsy one through six) and each sample was placed in formalin solution. A total of 35 sets of 6 biopsies were obtained producing a total of 210 samples. Samples were randomized and two independent pathologists who were blinded to the biopsy order assessed the histological quality of specimens. Specimens were evaluated for presence of full thickness mucosa, absence of fragmentation, crush artifact and diagnostic utility. Each pathologist then scored each specimen and the mean scores were used to compare the histological quality of the first biopsy vs. the sixth biopsy for each set. Results Our preliminary results include 12 of the 35 sets of biopsies. Using a paired sample t test, there was no significant difference between the mean score given to biopsy one and biopsy six for all twelve pairs [3.62 (SD 1.46) vs. 3.67 (SD 1.15), correlation factor=0.498 and p=.10). There was no significant difference between the first and sixth biopsy when comparing the presence of full thickness mucosa [0.59 (SD 0.49) vs. 0.59 (SD 0.44), p=.086], absence of fragmentation [0.50 (SD 0.50) vs. 0.73 (SD 0.34), p=0.06], absence of crush artifact [0.96 (SD 0.15) vs. 0.91 (SD 0.30), p=0.77), and specimen size [1.64 (SD 0.92) vs. 1.70 (SD 0.65), p=0.56). Conclusions No significant differences were found between the histological quality of the first biopsy and the sixth biopsy. Additional parameters such as specimen size, full thickness mucosa, absence of fragmentation and absence of crush artifact revealed no significant differences between the first and sixth biopsy. This preliminary data thus far shows that there is no difference between the histological quality when multiple biopsies are retrieved consecutively. Funding Agencies NoneNone
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".