Canadian paediatricians' approaches to managing patients with adverse events following immunization: The role of the Special Immunization Clinic network
Bibliographic record
Abstract
BACKGROUND: When moderate or severe adverse events occur after vaccination, physicians and patients may have concerns about future immunizations. Similar concerns arise in patients with underlying conditions whose risk for adverse events may differ from the general population. The Special Immunization Clinic (SIC) network was established in 2013 at 13 sites in Canada to provide expertise in the clinical evaluation and vaccination of these patients. OBJECTIVES: To assess referral patterns for patients with vaccine adverse events or potential vaccine contraindications among paediatricians and to assess the anticipated utilization of an SIC. METHODS: A 12-item questionnaire was distributed to paediatricians and subspecialists participating in the Canadian Paediatric Surveillance Program through monthly e-mail and mail contacts. RESULTS: The response rate was 24% (586 of 2490). Fifty-three percent of respondents practiced general paediatrics exclusively and 52% reported that they administer vaccines. In the previous 12 months, 26% of respondents had encountered children with challenging adverse events or potential vaccine contraindications in their practice and 29% had received referrals for such patients, including 27% of subspecialists. Overall, 69% of respondents indicated that they would be likely or very likely to refer patients to an SIC, and 34% indicated that they would have referred at least one patient to an SIC in the previous 12 months. CONCLUSIONS: Patients who experience challenging adverse events following immunization or potential vaccine contraindications are encountered by paediatricians and subspecialists in all practice settings. The SIC network will be able to respond to a clinical need and support paediatricians in managing these patients.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".