Bibliographic record
Abstract
Surgery for rectal cancer has resulted in unacceptably high local failure rates, and substantial morbidity and mortality. In an attempt to reduce the high frequency of local recurrence, perioperative radiotherapy has been used extensively, alone or in combination with chemotherapy. The local recurrence rate has been reduced dramatically with the use of radiotherapy, and provided that the dose is high enough and given preoperatively, the reduction rate has been about 50%. Despite that a higher dose is used in postoperative radiotherapy, the reduced recurrence rate is not that prominent. The reduced recurrence rate demonstrated after preoperative radiotherapy has a positive influence on survival, which has not been seen when radiotherapy is given postoperatively. However, when postoperative irradiation has been combined with chemotherapy, a survival benefit has been demonstrated. With modern radiation techniques, preoperative radiotherapy can be delivered without any substantial increase in postoperative mortality or morbidity, and a low rate of late toxicity, provided that the radiation technique is optimal. The main question is whether radiotherapy is necessary, provided that surgery is optimized. With standard surgery, the average local recurrence rate is 29% in all reported controlled trials. With optimal surgery, from institutional series, this figure is about 10%. Other questions to be answered are whether superfractionated or standard fractionation should be used in radiotherapy and exactly to whom it should be given.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".