Bibliographic record
Abstract
The aim of this study was to determine whether cervical diameter changes with age, parity or hormonal status, as we postulate that the cervical size can influence pelvic organ prolapse quantification (POPQ) staging, as apical stage 0 assumes a cervical diameter of ≤2 cm. We reviewed all hysterectomies performed at a single university-affiliated hospital and compared ex vivo cervical diameter based on parity, menopausal status and hormonal exposure. Specimen results were available for 127 women, mean age 48 years (rage 24-89), half were parous. Most (83%) surgical indications were benign. Of the 77 women whose menopausal status was known, 25 were postmenopausal. The mean cervical diameter was greater in women <55 years [3.5 cm, 95% confidence interval (CI) (3.4-3.6) vs 2.8 cm 95%CI (2.7-2.9)]. Parity was also associated with greater cervical diameter [3.4 cm, 95%CI (3.2- 3.6) vs 3.0 cm 95%CI (2.7-3.3)]. Only three women had a cervix ≤2 cm. The cervical diameter is considerably larger in younger and parous women. Consequently, its size may, conceptually, affect the apical staging of POPQ. Such misclassification could obscure identification and affect interpretation of studies evaluating the natural history or outcomes following surgical treatment of prolapse.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".