Using breath analysis as a screening tool to detect gastric cancer: A systematic review.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
= 3028) involving all technologies reported quantitative results, with sensitivities ranging from 67%-100% and specificities from 71%-98%. The summary sensitivity across six studies utilizing MS-based breath analysis methods was 82.4% (95% CI: 78%-86%); summary specificity was 91.3% (95% CI: 83%-96%). Based on these values, we estimated that screening with MS-based breath tests could lower the number needed to screen (NNS) by more than eight-fold in the 15 countries with the highest prevalence of gastric cancer.Breath analysis is a promising method for gastric cancer detection with good diagnostic performance and potential to decrease the NNS for endoscopy-based gastric cancer detection. However, due to the heterogeneity of breath analysis technologies, rigorous studies with standardized, reproducible methods are needed to evaluate the clinical applicability of these technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it