Optimal preoperative assessment and surgery for rectal cancer may greatly limit the need for radiotherapy
Bibliographic record
Abstract
BACKGROUND: Radiation is being used increasingly in the management of patients with rectal cancer. Over the past decade the Basingstoke Colorectal Research Unit has combined precision total mesorectal excision with the highly selective use of preoperative radiotherapy. METHODS: One hundred and fifty consecutive patients who underwent major surgical excision for cancers of all stages comprised the study group. Preoperative clinical assessment was based largely on tumour size, fixation and distance from the anal verge. Only preoperative radiotherapy was considered and this only for tumours judged to be at high risk of mesorectal fascia involvement. RESULTS: During a 5-year period 35 of 150 patients were selected for preoperative irradiation. In the non-irradiated patients the local recurrence rate after a median follow-up period of 870 (range 51-1903) days was 2.6 per cent (three of 115 patients), compared with 17.1 per cent (six of 35 patients) in those chosen for irradiation. Sixty patients (52.2 per cent) who were not irradiated were node positive. The local recurrence rate for the whole group was 6.0 per cent. CONCLUSION: The great majority of patients undergoing major excision for rectal cancer can be managed without radiation therapy if the preoperative assessment of the mesorectal fascia and surgery are performed optimally.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".