Routine pelvic examination during front-line chemotherapy for ovarian cancer: should it play a role?
Bibliographic record
Abstract
OBJECTIVE: To determine if pelvic examination affected management in patients undergoing first-line chemotherapy for ovarian cancer and to determine a threshold of change in tumor size reliably detectable by pelvic examination. STUDY DESIGN: We reviewed 501 encounters among 47 women with ovarian cancer to see if pelvic examination prompted a management change. Clinicians then evaluated synthetic model "tumors" and were retested at intervals of 3-48 hours to determine change needed for reliable detection. RESULTS: The median number of examinations was 3 during 8 cycles of chemotherapy. Fifteen examinations (10.5%) revealed palpable anomalies, attributable to known tumor in 10 instances. The most common events preceding management change were elevation in serum CA-125 (57%) or chemotherapy toxicity (20%). No changes were made based on pelvic examination alone. When assessing "tumor" volume in a model, estimates ranged from 33-309% of actual volume. Determination of volume change following a delay was poor. No reliable threshold of detection of volume change was established. CONCLUSION: Pelvic examination findings rarely dictated management changes in this study. Further, our results call into question the potential of routine pelvic examination to add significantly to clinical management during initial treatment given the wide range of error in "tumor" size estimates.
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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 | 0.002 | 0.024 |
| 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 | 0.001 |
| 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".