Patient navigation: improving timeliness in the diagnosis of breast abnormalities.
Bibliographic record
Abstract
OBJECTIVE: Patient navigation is a process that provides assistance to referring physicians in arranging further investigations and consultation for defined patient groups. This can facilitate timely investigations and potentially minimize delays. The purpose of this study was to determine the impact of patient navigation on timeliness in the diagnosis of breast abnormalities. METHODS: We retrospectively studied a cohort of 536 women who underwent breast core biopsy at our institution during comparable 6-month periods in 1999 and 2000 to determine the effects of patient navigation, age, and biopsy result on the wait for a biopsy after diagnostic imaging. Patient navigation was used for all women referred through the provincial breast cancer screening program. Navigation was unavailable to patients directly referred by physicians in 1999. In 2000, the program was expanded to encompass all patients. RESULTS: From 1999 to 2000, the median wait for a biopsy remained relatively stable for "navigated" screening patients at 12 days (n = 97) and 13 days (n = 133), respectively. The introduction of patient navigation for directly referred patients resulted in a statistically significant decrease in waiting times, from 20 days (n = 144) in 1999 to 14 days (n = 162) in 2000. Age and biopsy results were statistically significant variables, but their effect on the group data was negligible relative to that of navigation. CONCLUSIONS: Patient navigation significantly improves timeliness in the diagnosis of breast abnormalities and can potentially improve quality of life with more timely reassurance for women with benign conditions and earlier treatment for those with malignancy.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".