Mammography screening: how far is too far?
Bibliographic record
Abstract
INTRODUCTION: This study answers the question: 'How far must a Canadian woman travel before the risk of a motor vehicle accident (MVA) outweighs the benefits of mammography screening?'. METHODS: Numbers needed to screen and false positive rates were extracted from information in the breast screening guidelines from the Canadian Task Force on screening for breast cancer. Motor vehicle accidents per billion vehicle kilometres were extracted from Transport Canada. The charts of women undergoing screening mammograms were reviewed to determine the average number of extra trips generated from a false positive mammogram. A formula was devised to determine when the distance travelled and risk of MVA outweighed the benefits of mammogram screening. RESULTS: How far a woman would need travel before the risk of that travel outweighed the benefits of screening mammography is determined by the province in which she lives (location) and her age. The distance of a round trip before the risk of travel outweighed the benefit of screening mammography varied from 65 km to 1151 km, according the patient's age and location. CONCLUSION: Travel risk is rarely discussed in recommending screening examinations. Nevertheless the benefits of screening can be outweighed by the risk of travel. Knowledge of travel risk is essential before recommending screening procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".