Processes of Care in Autogenous Breast Reconstruction with Pedicled TRAM Flaps
Bibliographic record
Abstract
BACKGROUND: A multidisciplinary patient care plan was developed to facilitate early discharge following autogenous breast reconstruction and included (1) preadmission patient education, (2) perioperative multimodal pain management, (3) intraoperative nerve blocks, and (4) postdischarge telephone advice. This study evaluated the success of this care plan in the first 18 months after its implementation. METHODS: A retrospective cohort study of all consecutive women undergoing pedicled transverse rectus abdominis myocutaneous (TRAM) flap breast reconstruction (November of 2009 to May of 2011) was performed. The primary outcome was time to discharge; secondary outcomes included complications, readmission, and self-report pain at discharge. Predictors of discharge time were analyzed using stepwise multivariable regression modeling. RESULTS: Ninety-one women (mean age, 50.0 ± 8.5 years) underwent pedicled TRAM flap reconstruction (76 percent unilateral and 81 percent delayed), with 77 percent receiving the intended multimodal analgesia protocol. Mean time to discharge was 38.7 ± 27.6 hours. Overall, 40 percent of patients were discharged within 24 hours, but successful early discharge increased significantly over the study period. Key predictors of shorter time to discharge were use of multimodal analgesia, lower American Society of Anesthesiologists class, and surgery more than 6 months after implementation of the care plan. CONCLUSIONS: The authors' initial experience has supported the safety and feasibility of expedited discharge following pedicled TRAM flap breast reconstruction, with adherence to the authors' care plan improving steadily over the study period. Multimodal pain management proved a key modifiable factor in facilitating early discharge. A prospective study is currently underway to evaluate patient-reported quality of recovery following ambulatory surgery in this population.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".