Multidisciplinary assessment for immediate breast reconstruction: A new approach
Bibliographic record
Abstract
Aim Rates of immediate breast reconstruction (IBR) following a mastectomy in Canada have historically been low. To address this deficiency, our group established Canada's first multidisciplinary IBR clinic with the purpose of determining if the clinic increased our institutional rate of IBR and to evaluate the impact of IBR on quality of life in women with breast cancer. Patients and Methods A retrospective chart review was performed to determine the percentage of clinic attendees that had IBR and the total number of IBR procedures done at our institution in the first year of the clinic. This rate was compared to a historical control to determine if the initiation of the clinic correlated with an increase in the number of women undergoing IBR. Finally, patients who underwent IBR were administered the BREAST‐Q, a validated questionnaire, which was compared to a delayed breast reconstruction control group. Results Our institution's overall rate of IBR increased from 15 per cent in 2009 to 37 per cent in 2011. Women who underwent delayed reconstruction were found to have significantly reduced psychosocial and sexual wellbeing preoperatively. Conclusion A high rate of IBR is obtainable with increased awareness and a process to facilitate a multidisciplinary approach to surgically treating women with breast cancer.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".