Qualitative assessment of patient experiences related to extended pelvic resection for rectal cancer
Bibliographic record
Abstract
BACKGROUND: Patients with locally advanced rectal cancer (LARC) and locally recurrent rectal cancer (LRRC) represent a complex management challenge. While there is potential for cure in a subset of patients, the cost in terms of morbidity can be high. Few descriptions of the physical, psychological, social, and emotional experiences of these patients exist. METHODS: Face-to-face interviews were completed with ten LARC and LRRC patients treated with multimodal therapy that included surgery. Patient opinions and experiences were explored in depth until information redundancy and common themes were delineated using qualitative research methods. Clinical information was obtained from the database. RESULTS: Nine of the ten patients were male, seven had LARC, and the median age was 71. Six themes were identified from the patient interviews. Themes reflected patients' highly focused desire to seek wellness and cure, but also revealed misunderstanding of their disease biology, probability of cure, therapeutic options, and treatment morbidity. CONCLUSIONS: Patient experiences confirm that this is challenging treatment to complete, and that patient understanding of pre-operative information is incomplete. Our findings underscore the need for a multidisciplinary approach when managing this patient population, with emphasis on both supportive care needs and the technically skilled delivery of surgery, chemotherapy, and radiotherapy.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".