Digital versus screen film mammography: Impact on positive predictive values following transition.
Bibliographic record
Abstract
12 Background: The Calgary Health Region changed from screen film mammography (SFM) to digital mammography (DM) in 2005. This retrospective study was designed to determine the effect of this conversion on positive predictive values (PPV) for cancerous and precancerous breast lesions. Methods: In the Calgary region, biopsies for mammographic calcifications are only done at Foothills Medical Centre (FMC) by a small group of mammographers employing homogeneous techniques. From FMC’s database, we reviewed core biopsy data for mammographic calcifications in the years 2002-2004 (SFM years) and 2008-2010 (DM years). Mammographic masses were excluded. We determined PPVs for each set of years for detection of cancerous lesions (PPV3for calcifications). We further calculated the PPVs of SFM and DM for detection of high-risk lesions, including ADH, ALH, LCIS, and papilloma collectively (precancerous lesions). The detection rates of benign lesions (excluding precancerous lesions) after biopsy were also determined. Statistical analysis was performed using two-tail z-tests. Results: 3,778 biopsies in 3,544 patients were reviewed. The difference in overall detection rate of cancer after biopsy for mammographic calcification between SFM (PPV3 = 24.7%) and DM (PPV3 = 23.8%) was not statistically significant (p = .53). On further analysis, the PPV for precancerous lesions increased (p < .0001) in DM (11.6%) versus SFM (7.8%). No significant difference (p = .065) was found in detection of benign lesions. Conclusions: In comparing DM to SFM, we found no significant change in PPV3 with respect to calcifications. However, with DM, there was a statistically significant increase in detection of lesions considered at risk for future malignancy. Given that the natural history of these premalignant lesions is incompletely understood, the significance of this finding is in question. This potential trend could be further strengthened by determining PPV1for cancerous and precancerous lesions with respect to calcifications. [Table: see text]
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".