Reducing medication errors
Bibliographic record
Abstract
Hospital readmissions place a heavy burden on healthcare in Canada. Communication errors often occur at vulnerable time points like peridischarge, and these can lead to downstream adverse drug events necessitating rehospitalization. Existing mechanisms to address this problem still have room for improvement. Discharge summaries, for example, may be late, erroneous, or incomplete. Medication reconciliation processes directly integrated with electronic medical records have demonstrated advantages in terms of legibility and timeliness but unfortunately encourage risky behaviours like indiscriminate copy and pasting, leading to new errors of a different sort. A single unified medication list that could be kept on a patient’s person at all times can ensure that medication information is always present at medical appointments and is synchronously updated among all involved practitioners. Near-field communications (NFC) may facilitate the creation and maintenance of such a list. It is a relatively new wireless technology that has advantages over radio-frequency identification and Bluetooth. It allows information to be recorded to and read from objects as small and thin as a sticker. Technologies based on NFC may prove useful for consolidation patient health information into “one source of truth”.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".