A prospective model of care for breast cancer rehabilitation: Postoperative and postreconstructive issues
Bibliographic record
Abstract
Appropriate and timely rehabilitation is vital in the recovery from breast cancer surgeries, including breast conserving surgery, mastectomy, axillary lymph node dissection (ALND), and breast reconstruction. This article describes the incidence, prevalence, risk factors and time course for early postoperative effects and the role of prospective surveillance as a rehabilitation strategy to prevent and mitigate them. The most common early postoperative effects include wound issues such as cellulitis, flap necrosis, abscess, dehiscence, hematoma, and seroma. Appropriate treatment is necessary to avoid delay in wound healing that may increase the risk of long-term morbidity, unduly postpone systemic and radiation therapy, and delay rehabilitation. The presence of upper quarter dysfunction (UQD), defined as restricted upper quarter mobility, pain, lymphedema, and impaired sensation and strength, has been reported in over half of survivors after treatment for breast cancer. Moreover, evidence suggests that survivors who undergo breast reconstruction may be at higher risk of UQD. Ensuring the survivor's optimum functioning in the early postoperative time period is critical in the overall recovery from breast cancer. The formal collection of objective measures along with patient-reported outcome measures is recommended for the early detection of postoperative morbidity. Prospective surveillance, including preoperative assessment and structured surveillance, allows for early identification and timely rehabilitation. Early evidence supports a prospective approach to address and minimize postoperative effects.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".