Bibliographic record
Abstract
Public awareness of the need for colorectal cancer (CRC) screening is growing thanks to media personalities such as Katie Couric (1), and other publicity drives. Many Canadian provinces have responded to this by developing CRC screening programs. The model most provinces have considered is the fecal occult blood test (FOBT), in line with recommendations by Health Canada (2). These initiatives are welcomed, although FOBTs only reduce CRC mortality by 15% to 25%, and screening programs that prevent CRC, as well as detect the disease early, may be of greater benefit. The current alternative screening modalities are flexible sigmoidoscopy (FS) and colonoscopy (3). FS detects adenomatous polyps and malignancy up to the splenic flexure, where two-thirds of all CRCs are located. Therefore, the removal of adenomatous polyps should reduce the incidence of CRC. FS is currently being evaluated in three randomized controlled trials (RCTs) (4–6) assessing almost 360,000 patients. The stage that CRC is detected is earlier than seen with symptomatic cancers (4–6). The impact of FS on CRC incidence and mortality during follow-up will be reported in the near future. The advantage of FS is that the bowel preparation required is less rigorous, and the procedure is easier and quicker to perform than a colonoscopy, with no sedation required. On the other hand, FS will potentially miss right-sided lesions but colonoscopy views the whole colon; thus, colonoscopy is probably the most effective strategy. However, the cost of offering colonoscopy as a screening program is prohibitive in the Canadian health care setting. FS would also be difficult to deliver in Canada because there are insufficient clinicians to provide the service (7) and their time would be expensive. FS is relatively straightforward to perform, and if a less expensive section of the health care workforce could deliver this service then FS could be a viable screening option.
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 | 0.047 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".