Endocervical sampling using brush versus curette: a single centre experience and literature review
Bibliographic record
Abstract
Endocervical sampling is performed traditionally with an endocervical curette (ECC). The current study objective is to compare the histopathological performance of endocervical brush (ECB) and endocervical curette (ECC). A retrospective review was performed including patients included that underwent colposcopy with endocervical sampling using either method. A total of 127 samples were obtained with ECC and 98 with ECB. Histopathological diagnosis was obtained in 124 (97.6%) ECC samples and in 94 (95.9%) ECB samples (p = 0.46). The incidence of benign results was similar between ECC and ECB (117 (92.1%) versus 88 (89.8%) respectively (p = 0.28)). When combining information from endocervical sampling with cervical biopsies, the detection rate of high-grade pathologies was similar between the groups with 14 cases (17.7%) for ECC and 8 cases (17.0%) for ECB (p = 0.43). A scope review of the topic was performed, illustrating that studies favour either method. In conclusion, ECB and ECC perform similarly for providing a histopathological diagnosis on endocervical samples.IMPACT STATEMENTWhat is already known on this subject? Endocervical samples in colposcopy were traditionally obtained using an endocervical curette. Similarly, a brush can be used for histological sampling of the endocervical canal. However, it is unclear how the ability to obtain a histopathological diagnosis compares between the two techniques.What do the results of this study add? This single-institution experience with using endocervical brush and curette for endocervical sampling finds that both methods are acceptable and have a high ability to provide a histopathological diagnosis. Precisely, 4.1% of brush and 2.4% of curette samples had insufficient tissue.What are the implications of these findings for clinical practice and further research? The endocervical brush is an adequate sampling method for colposcopy, and can be safely used instead of the curette, based on clinician preference. Further studies could investigate how these methods compare from a patient perspective.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".