Canadian research finds automated calling system helps identify adverse drug reactions
Bibliographic record
Abstract
Using automated phone calls to follow up with patients after they receive a prescription can help uncover adverse drug events (ADEs), according to new Canadian research. The study, published in the online version of JAMA Internal Medicine on February 4, 2013, was led by Alan Forster, Scientific Director of Performance Measurement at Ottawa Hospital. According to Dr. Forster, the study was the first to use an automated calling system to check for adverse drug reactions. The research involved 629 patients at family health care practices in Quebec. The patients were contacted 3 days after receiving their prescription medication and again after 17 days, and responded to recorded questions that asked them about problems or new symptoms that had occurred after beginning to take the drug. Study researchers then contacted the patients after 21 days to get more detailed information. The study found that the automated phone call system identified 58 out of 125 ADEs (46%). The system offered the patients the chance to receive a phone call from a pharmacist to have a further discussion about their medication; about one-third of the patients asked for such a call. A journal editorial accompanying the study called the findings promising, but noted that automated calls alone have limitations, given that slightly fewer than half of ADEs were identified. The Canadian Institutes of Health Research (CIHR) is funding a randomized control trial of the system to gather more information on potential benefits and cost-effectiveness.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".