The challenges of providing rehabilitation for patients undergoing sacrectomy: two case reports
Bibliographic record
Abstract
BACKGROUND: Sacral neoplasms often present as large masses that often require sacrectomy. Multiple sacral nerve roots may be compromised post-sacrectomy and postoperative complications may result in impaired mobility, pain, orthostasis, and neurogenic bowel and bladder. CASE SERIES: Case 1, 58 year-old female with a sacral solitary fibrous tumor underwent a high-level sacrectomy and bilateral gluteal muscle flaps. Her rehabilitation course included management of pain, orthostasis, and neurogenic bowel and bladder. Case 2, 67 year-old male with sacral chordoma underwent high-level sacrectomy and bilateral gluteal muscle flaps. His rehabilitation course was complicated by refractory orthostatic hypotension, pain, and wound impairment, which resulted in slow rehabilitation progression and bowel and bladder training. Progression of activity in both cases was limited by surgical restrictions to support wound healing. CLINICAL REHABILITATION IMPACT: Multidisciplinary efforts after a sacrectomy are vital to successful rehabilitation. Highly functional outcomes are seen, including independent bowel and bladder management and return to preoperative ambulatory status.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".