Bibliographic record
Abstract
Even though medical errors are of great concern to both the public and health care, relatively little is known about the extent of the problem. In the US it is estimated that drug-related medication errors cost the health care system billions of dollars annually. “Medication errors do not just cause injury to patients,” says David U, president and CEO of the new Institute for Safe Medication Practices Canada (www .ismp-canada.org). “They also cost the health care system.” U says that Canada does not yet have accurate data on the extent of medication errors, but extrapolation from US data suggests that about 2% of hospitalized patients experience a preventable adverse drug event and that “an [estimated] 700 deaths per year result from medication errors.” To improve the situation, ISMP-Canada is developing several systems to track medication errors. One of the main ones is a self-reporting online form that allows practitioners to outline errors and “near misses.” The form asks for the reporting person's name, but ISMP-Canada guarantees that all information will remain confidential. Once enough data are collected, it hopes that analysis will point to ways to cut the number of drug-related errors. ISMP-Canada grew out of a similar initiative in the US, where ISMP-US (www.ismp.org) has a similar self-reporting system online. It also offers several other resources such as current drug alerts and a drug-testing service. Another American site is Bridge Medical Inc.'s MEDeRRORS (www .mederrors .com). It contains a library of abstracts, feature articles, an online CME course called “Anatomy of an Error” and several other resources. Back in Canada, physicians can keep track of current drug advisories through Health Canada's Adverse Drug Reaction Newsletter (www.hc-sc.gc.ca/hpb-dgps/therapeut/htmleng/cadrn wsletter .html). You can also check CMAJ's own drug advisories page at www.cma.ca/cmaj/fda-advisory/index.htm. — Michael OReilly, ten.ylliero@ekim
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.629 | 0.434 |
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".