Bibliographic record
Abstract
In Canada, approximately 20,000 people are diagnosed annually with colon or rectal cancer, and cancer in these two related sites is the second leading cause of cancer deaths [1]. A similar pattern is present in other jurisdictions around the world [2, 3]. Surgical removal of the appropriate bowel segment and attached lymphatic basin is the cornerstone of curative therapy. Unfortunately, many patients do not undergo such resections due to the presence of advanced unresectable disease or underlying patient comorbidities. Population-based data from Ontario, Canada (population 13 million) show that 18 % and 24 % of patients with colon and rectal cancer, respectively, do not undergo a resection of their tumor [4]. Other data, also from Ontario, suggest that 10 % of patients undergoing resection of their primary colon or rectal tumor have stage IV or metastatic disease [5] (see Table 36.1). Rarely is stage IV disease curable. Patients who present with metastatic disease do not enter surveillance regimens, but may enter testing regimens that monitor response to therapy. One may estimate then that, in Ontario and likely in many other large populations, approximately 70 % of patients with colon or rectal cancer undergo resective surgery for cure. This chapter addresses surveillance for these patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".