Efficacy of Doppler Ultrasound in Detection of Ovarian Malignancy
Bibliographic record
Abstract
Objective: To evaluate the accuracy of colour Doppler USG and b-mode USG preoperatively in detection of ovarian malignancy using histopathological diagnosis as gold standard Methodology: This cross-sectional study was conducted at Fauji Foundation hospital Rawalpindi from June, 2017 to Jan, 2020 after seeking the ethical approval from the hospital ethical committee A total of 96 female patients having adnexal masses on ultrasound were included in study. Enrollment in the study was subjected to written informed consent. Patients having adnexal mass of non-ovarian origin, patients not fit for surgery and lost to follow up were excluded from study. History taking and examination was followed by ultrasound and color-doppler of each patient. Color Doppler sonography was carried out with real-time ultrasound and Doppler scanner unit. All the included patients were undergone laparotomy after pre op workup. Histopathologies of all patients were traced. To avoid observer error, a designated trained operator performed doppler ultrasound from hospital own resource by using Toshiba Xario colour Doppler .Histopathology was also performed by hospital pathology laboratoty by trained histopathologist. All the included patients were undergone laparotomy after pre op workup. Histopathologies of all patients were traced .All the relevant findings were recorded in the pre-designed proforma. Data was entered and analysed in SPSS Version-26. Result: The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of Doppler with grayscale USG in detecting ovarian malignancy were calculated. The specificity was found to be 90.3% and sensitivity was 79.2%. Positive and negative predicative values were 92.9% and 73.1% respectively. Conclusion: Based upon the study findings, the Doppler USG’s reliability can be established for detection of ovarian malignancies. Keywords: Adnexal mass, Doppler, Ultrasonography, Ovarian malignancy
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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 | 0.005 | 0.020 |
| 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 | 0.000 |
| 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".