The contribution of currently available high resolution infra-red imaging to the detection of stage I and II breast cancer
Bibliographic record
Abstract
By the late sixties, combined studies proposed that both the sensitivity and specificity of infrared imaging of the breast was approximately 85%. This data justified its introduction into the Breast Cancer Detection Demonstration Project. The initial enthusiasm for this technique rapidly waned in North America. The Ville Marie Breast Center has continued to use this technique as a component of our multi-modality imaging strategy in the detection of breast cancer. The recent acquisition of high resolution digital infrared technology along with the development of a standard protocol for image production and interpretation by qualified physicians has given us an opportunity to better assess its complementary role to clinical exam and mammography. In a recent series of early breast cancer patients, the combined use of both infrared imaging and mammography was particularly useful in the patients in whom mammography, though done in a fully accredited center, was uninformative. Adding infrared imaging to mammography increased the detection rate. When infrared imaging benefits from the same quality control recently imposed on mammography, it constitutes a safe and practical imaging modality that in some cases promoted an earlier detection of breast cancer than did mammography.
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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 | 0.004 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".