Report on the Expert Forum on using Information Technology to Facilitate Uptake and Impact of Colorectal Cancer Screening Guidelines
Bibliographic record
Abstract
The present report summarizes the proceedings of the pan-Canadian Expert Forum on Using Information Technology to Facilitate Uptake and Impact of Colorectal Cancer Screening Guidelines, which was held in Montreal, Quebec, November 18 to 19, 2011. The meeting assembled a multidisciplinary group of family physicians, gastroenterologists, nurses, patients, foundation representatives, screening program administrators and researchers to discuss the development of a mechanism or strategy that would permit the collection of comparable data by all colorectal cancer (CRC) screening programs, which would not only support the needs of each program but also provide a national perspective. The overarching theme of the meeting was 'designing a national approach to computerized electronic data collection and dissemination for CRC screening that would improve knowledge transfer across the continuum of preventive health care'. The forum encouraged presentations on clinical, research and technical topics. The meeting fostered valuable cross-disciplinary communication and delivered the message that it is essential to develop a national health informatics approach for CRC screening data collection and dissemination to support provincial CRC screening programs.
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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".