Efficacy of single-incision needle-knife biopsy for sampling subepithelial lesions
Bibliographic record
Abstract
Abstract Background and study aims Single-incision needle-knife (SINK) biopsy is a diagnostic method for acquiring tissue samples for subepithelial lesions (SELs). A single linear incision is made in the overlying mucosa and tissue samples are obtained by passing conventional biopsy forceps through the opening and deep into the lesion. The aim of this study was to describe the efficacy and safety of this technique. Patients and methods Consecutive patients who underwent SINK biopsy for an upper gastrointestinal SEL between October 2013 and September 2015 were retrospectively reviewed. Results Forty-nine patients underwent 50 SINK biopsies. Sufficient sampling for a definite pathologic diagnosis was obtained in 42 (86 %) cases, with 91 % (40/44) having sufficient sample to perform immunohistochemistry when deemed clinically relevant. Of the 26 patients with prior non-diagnostic biopsies or FNA, a specific diagnosis was obtained in 85 % (22/26). There were no significant adverse events. Conclusions SINK biopsy is a safe and feasible strategy for obtaining a definitive tissue diagnosis with immunohistochemistry for SELs.
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 | 0.003 | 0.010 |
| 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 | 0.001 |
| 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".