Bibliographic record
Abstract
OBJECTIVE: To review the adequacy and diagnostic categories of the Bethesda system for reporting Pap test results (cervicovaginal cytology) and summarize management options. QUALITY OF EVIDENCE: The latest research evidence and guidelines from both international and Canadian sources are reviewed. With a few exceptions, good evidence supports particular management approaches for each adequacy statement and diagnostic category. MAIN MESSAGE: Women with unsatisfactory Pap smears should be re-examined and retested. Women with satisfactory smears and a diagnosis of "within normal limits" (WNL) or "benign cellular changes" (BCC) should be retested only at recommended screening intervals. Women with "satisfactory but limited by..." results and a diagnosis of WNL or BCC should have individualized follow up. Women with diagnoses of high-grade squamous intraepithelial lesions, atypical glandular cells of uncertain significance, or malignancy should have further investigation (colposcopy). Optimal management of asymptomatic women with normal cervices and reports of atypical squamous cells of uncertain significance or low-grade squamous intraepithelial lesions is still controversial. CONCLUSION: Management of women following Pap tests is determined by both the adequacy of the test and diagnoses based on the results.
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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.053 |
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".