Bibliographic record
Abstract
We are writing about Dr Michelle Greiver’s article, “Practice Tips. E-mailing patients,” in the September 2006 issue.1 E-mail communication between physicians and patients is indeed a growing trend, and clear guidelines are required to ensure patient safety and confidentiality are maintained. For these reasons, in February 2005, the Canadian Medical Association (CMA) created a policy on Physician Guidelines for Online Communication with Patients that outlines the best Canadian practices. The policy is available in English and French on the CMA website at http://policybase.cma.ca/dbtw-wpd/PolicyPDF/PD05-03.pdf. In addition the CMA has also developed a privacy tool called the CMA Privacy Wizard, which allows physicians to rapidly create a personalized patient confidentiality policy for their practice. This tool is also available free in both official languages on the CMA website at http://www.cma.ca/index.cfm/ci_id/40833/la_id/1.htm. (Please note that registering for cma.ca is free for all physicians in Canada.)
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.341 | 0.387 |
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".