Sensitivity and Specificity of the Pap Smear for Glandular Lesions of the Cervix and Endometrium
Bibliographic record
Abstract
OBJECTIVE: To investigate the sensitivity and specificity of the Pap smear for detection of adenocarcinoma in situ of the cervix (AIS), endocervical adenocarcinoma (ECAC) and endometrial adenocarcinoma (EAC) as well as the overall specificity of the smear for detection of glandular lesions in general. STUDY DESIGN: Computer records of the laboratory of the QE II Health Sciences Center, Halifax, were searched for patients who had AIS, ECAC or EAC diagnosed on histology between June 1, 1999, and May 31, 2001 and who had had a Pap smear within the preceding year. Computer records were also searched for patients who had a Pap smear result consisting of suspicious or positive for AIS or adenocarcinoma (AC) with subsequent tissue diagnosis during the same time. The histologic and cytologic findings were correlated. RESULTS: One hundred percent of patients with AIS, 80% with ECAC and 22% with EAC on histology had positive findings on a Pap smear performed within a year of the histologic diagnosis. One hundred percent of patients with a Pap smear result consisting of suspicious or positive for AIS or AC and follow-up histology had a lesion on histology: 13% AIS, 13% ECAC, 37% EAC, 23% other AC, 10% high grade squamous lesion and 0.3% low grade squamous lesion. CONCLUSION: This study confirmed the good overall specificity of the Pap smear for glandular lesions in general. It also confirmed the good sensitivity for glandular lesions of the cervix and the poor sensitivity for glandular lesions of the endometrium. It thus confirmed that the Pap smear is not an effective screening tool for endometrial AC, and that the quest for alternative screening methods should continue.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".