978 Canadian Family Physician • Le Médecin de famille canadien Vol 54: july • juillet 2008 Letters
Bibliographic record
Abstract
Screening change Modern medicine and ongoing research have dem-onstrated time and time again that often what we believed was correct in the past is no longer valid. New evidence brought to us by current research enables us to change our practice accordingly and provide patients with updated medical therapies and advice. We all agree that colorectal cancer (CRC) mortality cannot be ignored in a first-world country such as Canada. Colorectal cancer is not only treatable in its early stages, but is also preventable with different screening strategies such as fecal occult blood testing and colonoscopy. It is true that screening has potential harms and costs, but these will improve with implementation. Can we continue to refute CRC screening using argu-ments from 40 years ago?1 I do not believe that it is pos-sible. For my part, I discuss the benefits and potential risks of different screening approaches with my patients, and initiate discussions about CRC screening in particu-lar to increase awareness of this preventable cancer. I do not think we do enough CRC screening compared with other developed countries, and instead depend too much on less expensive approaches that might not be as reliable.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.134 | 0.020 |
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".