EVALUATING THE EFFICACY OF A VIRTUAl PAEDIATRIC ADVERSE DRUG REACTION CLINIC
Bibliographic record
Abstract
Adverse drug reactions (ADRs) are a common and significant cause of morbidity and mortality, yet access to specialized ADR services for children is limited. The purpose of this study was to evaluate the efficacy of, and satisfaction with, a virtual paediatric ADR clinic based in London, ON during the COVID-19 pandemic. This was done by extracting patient information from an online database and conducting brief satisfaction surveys with patient families and referring clinicians. We found that 225 ADRs were assessed virtually over three years, with an average referral time of 23 days and 43.6% of patients residing in distant rural and urban communities. Further testing (most commonly DPT, RAST, LTA) was recommended for 89.3% of patients. While a number of results are pending, 17.2% and 43.5% of tests ordered were positive and negative respectively. A high level of satisfaction was reported by patient families (95.0%) and referring physicians (93.3%) that responded to the survey. This study demonstrated the ability of a virtual clinic to efficiently assess and triage children with a suspected ADR and suggests that telemedicine is a feasible way to improve access to specialized ADR services for children.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".