The Olfaction Ability of Medical Detection Canine to Detect Prostate Cancer From Urine Samples: Progress Captured in Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: To date, early cancer detection is considered vital to reduce the global cancer burden through low-cost, but accurate screening modalities. The anatomical positioning of prostate cancer (PCa) created a potentially distinctive diagnostic method through the identification of volatile organic compounds (VOCs) in urine, which might be detectable not by humans but by canine species. This review aimed to capture the potential of the medical detection canine (MDC) to detect PCa by providing its diagnostic accuracy estimation on urine odor testing. Methods: Databases, e.g., MEDLINE, Cochrane, ScienceDirect, and ProQuest, were searched to identify the studies. We focused on accessible original research, comparing the diagnostic utility of trained female MDC and histopathology examination as the gold standard for PCa diagnosis. The statistical analysis was performed in Meta-DiSc 1.4 and presented in diagnostic values, i.e., sensitivity (Sn), specificity (Sp), positive or negative likelihood ratio (LR+ or LR-), diagnostic odd ratio (DOR), and area under the curve (AUC) value, to conclude the Sn-Sp in a single outcome. Results: Female German Shepherds were the most commonly utilized MDC from the five studies included in the final analysis. We estimate the pooled diagnostic value of eight different MDCs, with the findings as follows: Sn (0.95 (0.94 - 0.97)), Sp (0.92 (0.90 - 0.93)), LR+ (4.48 (1.90 - 10.58)), LR- (0.12 (0.01 - 1.42)), DOR (35.39 (2.90 - 432.53)), and an AUC value of 0.9232. Conclusions: MDC's olfaction ability holds considerable potential on its diagnostic accuracies to distinguish the urine of PCa individuals by identifying its volatilome property.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".