Qualitative assessment of patient experiences following sacrectomy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The primary objective was to investigate patient experiences following sacral resection as a component of curative surgery for advanced rectal cancers, soft tissue and bone sarcomas. METHODS: Qualitative methods were used to examine the experiences, decision-making, quality of life, and supportive care needs of patients undergoing sacrectomy. Patients were identified from two prospective databases between 1999 and 2007. A semi-structured interview guide was generated and piloted. Patient interviews were transcribed verbatim and analyzed using standard qualitative research methodology. Grounded theory guided the generation of the interview guide and analysis. RESULTS: Twelve patients were interviewed (6 female, 32-82 years of age). The mean interview time was 34 min. Five themes were identified, including: (1) the life-changing impact of surgery on both patients' and their family's lives, (2) patient satisfaction with immediate care in hospital, (3) significant chronic pain related to sacrectomy, (4) patients' need for additional information regarding long-term recovery, and (5) patients' gratitude to be alive. CONCLUSIONS: Sacrectomy is a life-changing event for patients and their families. Patients undergoing sacrectomy need further information regarding the long-term consequences of this procedure. This need should be addressed in both preoperative multi-disciplinary consultations and at follow-up visits.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".