Improvement in the quality of care for patients with locally advanced breast cancer through implementation of an integrated electronic care pathway.
Bibliographic record
Abstract
207 Background: Locally advanced breast cancer (LABC) refers to the most advanced stage of non-metastatic tumours with an incidence of approximately 10% in newly diagnosed breast cancers. Currently, for optimal care, patients with LABC require a multidisciplinary approach including coordinated planning with medical, surgical and radiation oncologists. We created an interactive electronic care pathway and self populating quality assurance database at St. Michael’s Hospital (SMH) to facilitate multidisciplinary teams to track LABC patient histories and patient treatments in order to coordinate therapy effectively and expedite care (LABC E-PATH). Methods: This is an observational before-and-after cohort study of patients with LABC with a retrospective review pre-implementation and prospective collection of clinical data post-implementation. The completeness of workup and the timeliness of treatment pre- and post-implementation of the LABC E-PATH in May 2010 were assessed. Results: With the implementation of the LABC E-PATH in May 2010 at SMH, the delay between the identification of the patient as LABC and their referral to a medical oncologist for treatment for their LABC decreased from a median of 9 days pre-implementation, (range 0-780 days) to 1 day post-implementation, (range 0-52 days). The time between referral to medical oncologist and the start of their chemotherapy treatment decreased from a median of 12 days to 9 days (pre-implementation: range = 4 to 494, post-implementation: range = 0 to 39). All pre-treatment staging was completed faster post-implementation of the LABC e-path than pre-implementation, expediting time to initiation of chemotherapy. The number of referrals for LABC to the SMH program increased from < 1 patient per month to 5 patients per month post-implementation. Conclusions: The LABC E-PATH at SMH has achieved its goal of expediting care for this patient population. It has also ensured timely and appropriate resource allocation. This unique system may also be applied to other disease sites where coordination of a multi-disciplinary team is critical for appropriate patient management.
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 | 0.006 | 0.029 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".