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Record W1881229195 · doi:10.1017/s146039691500028x

Deep Inspiration Breath Hold for left-sided breast cancer: experience from the patient’s perspective

2015· article· en· W1881229195 on OpenAlexaff
Neil Mc Parland, Luminiţa Nica, Jenny Soo, Tara Menna

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsPerspective (graphical)ComprehensionMedicineMedical physicsAnxietyRadiologyComputer scienceArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.325
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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