Results from a Mammography Audit and Patient Attitudes Study at the Princess Margaret Hospital
Bibliographic record
Abstract
This paper reports the results from (1) a mammography audit from the Princess Margaret Hospital and 2) patient attitudes towards mammographs in the context of national risks of breast cancer. The audit allows for the quality of mammographic diagnoses to be assessed. Information on patient attitudes and follow-up to mammograms is required so that health care providers can better interact and educate patients. Between 2000 and 2002 about two percent of 4,322 patients were diagnosed with breast cancer. A family history, previous mastectomy, or a patient having a complaint was found to elevate the risk of breast cancer. In the telephone study, the cancer detection rate was 18.8 per 1000. The positive predictive value of mammogram reports was 15.4%; the recall rate was 12.2%. The biopsy yield was 32.1%. The sensitivity and specificity of mammograms in 2001 were 100% and 89.5% respectively. The telephone study of 1,092 previous patients found that doctors were a major influence in getting patients to have a mammogram. Many women were ignorant about essential aspects of mammography and this probably resulted in many of them not returning for subsequent mammograms. Patients who should have had follow-up appeared not to have followed the doctor's recommendation. National data on breast cancer indicated that women in New Providence have a higher risk of breast cancer compared to women in other Bahamian islands.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".