<p>Combined EsophaCap cytology and MUC2 immunohistochemistry for screening of intestinal metaplasia, dysplasia and carcinoma</p>
Bibliographic record
Abstract
Purpose: The incidence of esophageal adenocarcinoma (EAC) has increased by 700% in Western countries over the last 30 years. Although clinical guidelines call for endoscopic surveillance for EAC among high-risk populations, fewer than 5% of new EAC patients are under surveillance at the time of diagnosis. We studied the accuracy of combined cytopathology and MUC2 immunohistochemistry (IHC) for screening of Intestinal Metaplasia (IM), dysplasia and EAC, using specimens collected from the EsophaCap swallowable encapsulated cytology sponge from Canada and United States. Patients and methods: By comparing the EsophaCap cytological diagnosis with concurrent endoscopic biopsies performed on the same patients in 28 cases, we first built up the cytology diagnostic categories and criteria. Based on these criteria, 136 cases were evaluated by both cytology and MUC2 IHC with blinded to patient biopsy diagnosis. Results: We first set up categories and criteria for cytological diagnosis of EscophaCap samples. Based on these, we divided our evaluated cytological samples into two groups: non-IM group and IM or dysplasia or adenocarcinoma group. Using the biopsy as our gold standard to screen IM, dysplasia and EAC by combined cytology and MUC2 IHC, the sensitivity and specificity were 68% and 91%, respectively, which is in the range of clinically useful cytological screening tests such as the cervical Pap smear. Conclusions: Combined EsophaCap cytology and MUC2 IHC could be a good screening test for IM and Beyond. Keywords: Barrett’s esophagus, esophageal adenocarcinoma, cytology screening, MUC2 IHC, EsophaCap, intestinal metaplasia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".