Deep Inspiration Breath Hold for left-sided breast cancer: experience from the patient’s perspective
Bibliographic record
Abstract
Abstract Introduction The dosimetric benefits of Deep Inspiration Breath Hold (DIBH) in reducing cardiac dose are well documented, however reports on the patient’s personal experience with this technique are limited. The purpose of this research is to investigate DIBH from the patient’s perspective and to provide recommendations to further improve the patient experience. Materials and methods A questionnaire was used to record the patient’s comprehension of DIBH instructions and preparation for treatment. Levels of comfort, confidence and technical challenge were also recorded and an open-format question allowed patients to provide suggestions to improve the DIBH experience. Results The majority of patients do not find it difficult to hold their breath at the correct level during DIBH and confidence levels regarding ability to follow instructions are good. Comprehension of instructions, preparation to perform DIBH and treatment position comfort levels were universally graded positively. Conclusion The majority of patients reported a strong level of comprehension and preparation that allows them to confidently perform DIBH as planned. Establishment of a dedicated treatment team, consistent patient instructions, regular feedback and an opportunity to rehearse DIBH can help increase patient confidence and reduce anxiety.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".