Preoperative diagnosis of ovarian carcinoma with a novel monoclonal antibody
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to determine whether preoperative radioimmunoscintigraphy of complex ovarian masses with technetium Tc 99m MAb-170 (Tru-Scint AD; Biomira Inc, Edmonton, Alberta, Canada), a murine whole immunoglobulin G monoclonal antibody that has been found to have panadenocarcinoma affinity, would predict surgical findings. STUDY DESIGN: The age range of studied patients was 42 to 83 years (mean, 60.3 years). Planar computed tomographic imaging and single-photon emission computed tomographic imaging were performed at 15 minutes, 6 to 8 hours, and 18 to 24 hours after injection of 1000 MBq technetium Tc 99m MAb-170. Laparotomy was performed within 10 days. RESULTS: Eighteen patients had borderline or invasive ovarian cancers verified by histologic examination. All primary malignancies or deposits (including intrahepatic deposits) yielded positive results on radioimmunoscintigraphic imaging. Radioimmunoscintigraphy was able to identify serosal deposits not seen on computed tomographic or ultrasonographic scans. False-positive localization of the antibody was noted in 6 of the 9 patients with benign pathologic processes. CONCLUSION: It is possible to detect with technetium Tc 99m MAb-170 all patients who have cancer (including sites not seen on computed tomographic or ultrasonographic scan); however, the low specificity (33%) means that patients still require surgical verification of disease
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".