Assessing Post-Radiotherapy Handover Notes from a Family Physician Perspective
Bibliographic record
Abstract
Background: Across our province, post-radiotherapy (RT) handover notes are sent to family physicians (FPS) after RT. Based on previous FP feedback, we created a revised post-radiotherapy handover note with more information requested by FPS. The purpose of this study was to determine whether the revised handover note improved the note as a communication aid. Methods: Potential common and rare treatment side effects, oncologist contact information, and treatment intent were added to the revised handover note. Both versions were sent alongside a questionnaire to FPS. Paired t-tests were carried out to compare satisfaction differences. Results: There was a response rate of 37% for the questionnaires. Significantly greater clarity in the following categories was observed: responsibility for patient follow-up (mean score improvement of 1.2 on a 7-point Likert scale, p < 0.001), follow-up schedule (1.1, p < 0.001) as well as how and when to contact the oncologist (1.4, p = 0.001). Family physicians were also more content with how the institute transitioned care back to them (1.5, p = 0.012). Overall, FPS were generally satisfied with the content of the revised post-RT handover note and noted improvement over the previous version. The frequency of investigations and institute supports initiated such as counselling services were suggested further additions. Conclusions: The inclusion of potential treatment side effects, oncologist contact information, treatment intent and a well-laid out follow-up schedule were essential information needed by FPS for an effective post-RT completion note. With these additions, the revised post-RT handover note showed significant improvement.
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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 | 0.006 | 0.034 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".