Primary Care Use after Cancer Treatment: An Analysis of Linked Administrative Data
Bibliographic record
Abstract
Background: Primary care–led follow-up is a safe and acceptable alternative to oncologist-led follow-up. We sought to investigate patterns of primary care use during cancer follow-up care. Methods: We identified all persons in Nova Scotia, diagnosed with an invasive breast, prostate, colorectal, or gynecologic cancer between January 2006 and December 2013. We linked this dataset to cancer centre, hospital discharge abstracts, physicians’ billing, and census data. We identified a survivor cohort (n = 12,201), then descriptively examined primary care use during follow-up care. Multivariate Poisson and negative binomial regression, respectively, were used to examine primary care use for two outcomes: total number of primary care provider (pcp) visits (all reasons) and total number of cancer-specific pcp visits. Results: The mean numbers of pcp visits (all reasons) and cancer-specific pcp visits per year for survivors who did not receive cancer centre follow-up (cc-fup) were 8.12 and 0.43 visits, respectively, and for survivors who continued to receive cc-fup were 8.75 and 0.63 visits, respectively. Age, cancer type, stage at diagnosis, comorbidity scores, year of diagnosis, and receipt of cc-fup were associated with both outcomes. Compared with prostate cancer survivors, breast, colorectal, and gynecologic cancer survivors had, respectively, 56%, 69%, and 56% fewer expected cancer-specific PCP visits. Receipt of cc-fup increased the expected number of pcp visits (all reasons) by 12% and cancer-specific pcp visits by 50%. Conclusions: Primary care use was higher in survivors who continued to visit their oncology teams for follow-up. This suggests that survivors who remain with their oncology teams after treatment continue to have high needs not met by these teams alone.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".