Endoscopy and histology in inflammatory bowel diseases patients: Complementary or alternatives?—Author’s reply
Bibliographic record
Abstract
We read with great interest the editorial: “Endoscopy and histology in inflammatory bowel disease patients: complementary or alternatives?”1 and we thank Dr. D’Amico et al. for insightful comments. The clinical relevance of an endpoint that combines histopathological and endoscopic assessments of mucosal healing versus only endoscopic endpoint is a matter of increasing debate. We have reported that PICaSSO endoscopic score can accurately predict histologic remission without having to combine both measures and this is of significant practical benefit.2 However, we fully agree that we need randomized controlled trials before translating into routine clinical practice. In addition, we were aware of limited use of PICaSSO score given that this was initially, developed and validated using iSCAN platform (Pentax) which is not available in all the endoscopy unit.3, 4 To overcome this limitation, we have recently investigated PICaSSO reproducibility and validation by using narrow band imaging (NBI) Olympus and linked colour imaging/blue-laser imaging (LCI/BLI), Fujifilm and this has been recently published.5 After a brief training for PICaSSO, we determined the interobserver variability (ICC) in a group of both experienced and less experienced endoscopists who were asked to score colonoscopies videos. In both Virtual Electronic Chromoendoscopy (VCE) platforms (NBI, LCI/BLI) the ICC for PICaSSO and its subscores (mucosal and vascular) were either good or very good, and most importantly, always numerically higher than for Mayo Endoscopic Score (MES) and Ulcerative colitis (UC) endoscopic Index of severity (UCEIS). Furthermore, in both NBI and BLI/LCI groups PICaSSO showed a strong correlation with histology. These results confirm that PICaSSO is accurate and reliable score on all available VCE platforms.5 However, we have already previously tested interobserver agreement in experienced consultant and trainees who had no prior exposure to Electronic Virtual Chromoendoscopy, by using a short training module (colonoscopy video library: 30 cases reviewed pre-training and 30 post-training) and PICaSSO revealed good interobserver agreement across all levels of experience in even non-expert setting reaching high ICC.3 Notably training modules, representative of all the endoscopic mucosal and vascular findings and with all the endoscopy platforms, are now available.3, 5 In conclusion, PICaSSO made an important point in the current debate that using high-definition endoscopes and VCE to enhance mucosal and vascular details, discrepancy between endoscopy and histology has become small. Histology plays still a crucial role for the clinical management of UC and we should not think that endoscopy will replace histology but be complementary. However, we hope to pave the way to motivate the implementation of VCE with targeted “smart biopsies “ and eliminate the use of MES = 1 as endoscopic remission which is imprecise. Finally, PICASSO is an accurate endoscopic score and each of mucosal and vascular items describe a single features of healing and active inflammation. Hence is suitable for a computer aided diagnosis for standardisation and endoscopy reading.6 MI is part-funded by the NIHR Birmingham Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. None. Data available on request from the authors.
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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 | 0.007 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".