The prevalence and impact of oral complication in women during chemotherapy after surgery for breast cancer – A cross sectional study
Bibliographic record
Abstract
Background and aim: Mucositis is a well-known side effect to chemotherapy treatment after breast cancer surgery. The number of women who experience oral complication that is not classified as mucositis is less investigated as well as the impact of oral complication on the women’s quality of life. \textit{Aim:} To describe how many women with breast cancer report oral complications during their adjuvant chemotherapy with Ebirubicin, Cyclophosphamide and Taxotere or Taxol, to describe which oral complications the women report and the impact the oral complication has on women’s daily life and quality of life.Methods: A cross-sectional design was used. The women were invited to fill out a self-composed questionnaire at proximal 12 weeks after initiation of the treatment. The questionnaire had two scales to summarize information about oral complication and their impact on daily living was used. The questionnaire has been face- and content validated. Internal consistency was between 0.76 to 0.83.Results: All 101 women had experienced oral complications to some extent. A linear regression analysis has reviled that redness, coaching and changes of taste explained 74% of reported reduction in quality of life. There was a positive correlation between the sum of symptoms (number of symptoms and duration) and reported quality of life score r = .480 (p = .000).Conclusions: Oral complications was experienced by all women who were treated with CT after breast cancer surgery. Redness, coaching and changes of taste were significant contributors to reducing quality of life and need to be prevented during chemotherapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".