Approach to cervical polyps in primary care
Bibliographic record
Abstract
OBJECTIVE: To provide primary care providers (PCPs) with an approach for diagnosing and managing endocervical polyps, detailing a procedural technique for cervical polypectomy and outlining key information on when to refer to a gynecologist. SOURCES OF INFORMATION: This review and approach are based on the second author's clinical practice and available literature from 1994 to 2023. MAIN MESSAGE: Cervical polyps are commonly asymptomatic and benign, but can cause intermenstrual and postcoital bleeding. Cervical polyps alone are unlikely to be associated with dysplasia or malignancy; routine Papanicolaou and human papillomavirus tests remain the most important factors in identifying cervical dysplasia. For symptomatic patients, the lack of available literature to guide PCPs can result in unnecessary referrals to gynecology, long wait times, and associated costs to the health care system. Symptomatic endocervical polyps can be easily and painlessly removed by primary care clinicians in office using a ring-forceps polypectomy technique. CONCLUSION: Cervical polyps are common and generally do not require intervention if asymptomatic. Patients with cervical polyps should still participate in routine cervical cancer screening. Symptomatic cervical polyps in appropriate patients can be removed by PCPs and sent for histologic examination to avoid long wait times and unnecessary referrals to gynecology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".