Breast reconstruction following mastectomy for invasive breast cancer is strongly influenced by demographic factors in women in Victoria, Australia
Bibliographic record
Abstract
This study explored factors associated with the likelihood of reconstruction after unilateral mastectomy and the wellbeing of women after reconstruction. Data were from a questionnaire completed on average 1.8 years after diagnosis by 1429 women in the BUPA Health and Wellbeing After Breast Cancer Study. Logistic regression was used to model factors associated with reconstruction. The Psychological General Wellbeing Questionnaire was used to assess wellbeing. A total of 25.4% of 366 women who had a unilateral mastectomy had undergone a reconstruction nearly two years after diagnosis. Being younger (p<0.001), educated beyond school (p<0.04), living in the metropolitan area (p<0.001), having private health insurance (p=0.003), not having dependent children (p=0.004) and not having radiotherapy (p<0.001) explained just over 40% of the variation in reconstruction status. There was a modest difference between women who did and did not have a reconstruction in terms of wellbeing. Demographic factors strongly influence the likelihood of reconstruction after mastectomy.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".