Microfluidic devices as miniaturized screening and diagnostic approaches for gynecological cancers detection
Bibliographic record
Abstract
Gynecological cancers, including cervical, endometrial, and ovarian cancer, contribute to a significant portion of female cancer-related deaths. Despite advancements in cancer detection, these diseases continue to pose challenges due to limited cost-effective screening methods and late-stage diagnoses. This review paper focuses on the utilization of microfluidic devices (MFDs) as a cost-effective tool for diagnosing and screening gynecological cancers. MFDs are portable instruments capable of sample separation, extraction, dilution, mixing, and biomarker detection. Their compact size and efficiency make them advantageous for comprehensive sample analysis. The emergence of microfluidic point-of-care devices offers potential for developing biomarker-based screening technologies and facilitating early detection of gynecological cancers. This paper aims to consolidate the knowledge and findings surrounding the utilization of MFDs in gynecological cancer research by summarizing the current literatures that exist between 2006 and 2023. The review of previous research in this area will contribute to a comprehensive understanding of the recent state of utilizing MFDs for gynecological cancer screening and detection as it will shed light on the advancements made thus far and provide insights into the future prospects and potential directions of research in this field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".