Improving Patient Flow and Timeliness in the Diagnosis and Management of Breast Abnormalities: The Impact of a Rapid Diagnostic Unit
Bibliographic record
Abstract
BACKGROUND: Efforts to streamline the diagnosis and treatment of breast abnormalities are necessary to limit patient anxiety and expedite care. In the present study, we examined the effect of a rapid diagnostic unit (rdu) on wait times to clinical investigations and definitive treatment. METHODS: A retrospective before-after series, each considering a 1-year period, examined consecutive patients with suspicious breast lesions before and after initiation of the rdu. Patient consultations, clinical investigations, and lesion characteristics were captured from time of patient referral to initiation of definitive treatment. Outcomes included time (days) to clinical investigations, to delivery of diagnosis, and to management. Groups were compared using the Fisher exact test or Student t-test. RESULTS: The non-rdu group included 287 patients with 164 invasive breast carcinomas. The rdu group included 260 patients with 154 invasive carcinomas. The rdu patients had more single visits for biopsy (92% rdu vs. 78% non-rdu, p < 0.0001). The rdu group also had a significantly shorter wait time from initial consultation to delivery of diagnosis (mean: 2.1 days vs. 16.7 days, p = 0.0001) and a greater chance of receiving neoadjuvant chemotherapy (37% vs. 24%, p = 0.0106). Overall time from referral to management remained statistically unchanged (mean: 53 days with the rdu vs. 50 days without the rdu, p = 0.3806). CONCLUSIONS: Introduction of a rdu appears to reduce wait times to definitive diagnosis, but not to treatment initiation, suggesting that obstacles to care delivery can occur at several points along the diagnostic trajectory. Multipronged efforts to reduce system-related delays to definitive treatment are needed.
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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 | 0.007 | 0.041 |
| 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 | 0.001 | 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".