Guidelines unmet: Assessing gaps in breast cancer survivorship care
Bibliographic record
Abstract
BACKGROUND: In Canada, after completing their treatment at oncology centers in tertiary care facilities, most breast cancer patients are discharged and receive survivorship care from primary care providers (PCPs). Evidence-based guidelines exist to inform appropriate care for breast cancer survivor follow-up. OBJECTIVES: This study analyzed the concordance of breast cancer survivorship follow-up care by PCPs with recommended guidelines at an academic Family Health Team (FHT) in Ottawa. DESIGN: Retrospective chart review of electronic medical records of rostered patients from FHT. METHODS: Data was extracted from the charts of 60 breast cancer survivors. Concordance of breast cancer survivorship care by PCPs with evidence-based guidelines was established in three key survivorship domains: surveillance for recurrence or new cancers, management of treatment side effects and preventative health. RESULTS: PCPs provide care concordant with guidelines only 20% of the time, with areas such as preventative care at 86.7% concordance far better than management of side effects at 58.3% and oncological surveillance at 38.3%. Care did not significantly differ by age at diagnosis. CONCLUSION: These results highlight gaps in the current survivorship care delivery and function as a baseline for comparative analyses for future interventions to optimize survivorship follow-up care.
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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 | 0.029 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".