Utility of standardized letters in assisting primary care providers (PCPs) with the care of cancer survivors (CS).
Bibliographic record
Abstract
9593 Background: Survivorship care plans have strong face validity, but individualizing plans to each patient’s needs can be resource intensive. At our institution, a 1-page standardized letter that outlines the essential components of follow-up care is mailed to PCPs at the time of a patient’s discharge. This letter highlights the recommended frequency and interval of tests and physician visits. Our study aims were to 1) characterize PCPs’ attitudes regarding these letters and 2) identify potential strategies to improve this channel of communication with PCPs. Methods: Self-administered surveys were mailed to high-volume PCPs in British Columbia, defined as those whose practices followed >/=5 breast or colorectal CS in the preceding year. The survey asked about practice characteristics and PCPs’ views towards the content and format of these standardized letters. Logistic regression models were constructed to delineate factors associated with follow-up preferences. Results: Among 787 PCPs, 507 (64%) responded: median year since graduation was 27 (range 1-62), 67% were men, 38% had a faculty appointment, 71% practiced in a group, and 92% were paid fee-for-service. When asked about their perspectives regarding the care of CS, 388 (77%) indicated they were comfortable providing follow-up with 299 (74%) reporting that the standardized letter contained adequate information. In regression models, PCPs who were comfortable with cancer surveillance and those who graduated greater than 30 years ago were more likely to view the standardized letter as useful (OR 2.50, 95% CI 1.41-4.43 and OR 2.31, 95% CI 1.24-4.33, respectively) and important (OR 4.07, 95% CI 1.80-9.19 and OR 3.14, 95% CI 1.01-9.74, respectively). Among 103 (26%) PCPs who found the letter to be insufficient, most wanted additional details about the cancer diagnosis (88%), specific information on the toxicities of therapy (88%), and the estimated risk of recurrence (84%). Conclusions: Most PCPS were satisfied with a simple, standardized letter that outlines the necessary components of cancer follow-up. PCPs with less familiarity with cancer surveillance may be a target group that benefits most from individualized survivorship care plans.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".