Universal Tool for Vaccine Scheduling: Applications for Children and Adults
Bibliographic record
Abstract
To improve coverage against vaccine-preventable diseases for children and adults, and to aid caretakers and providers in making appropriate and timely vaccination decisions, Georgia Institute of Technology collaborated with the Centers for Disease Control and Prevention to develop decision support tools for creating optimized catch-up immunization schedules for four target groups: children through age 6, adolescents ages 7 through 18, adults ages 19 and over in the United States, and children and adolescents through age 19 in Canada. Our solution to the catch-up scheduling problem for each targeted group determines the best coverage schedule for each individual given his (her) vaccination history and age. If an individual misses one or more doses of a recommended vaccine, a health-care professional is typically responsible for generating a feasible catch-up schedule that optimizes the person's coverage against vaccine-preventable diseases, a task that is often challenging and time consuming. Inappropriate schedules could prevent some individuals from being vaccinated in a timely manner, potentially increasing their risk of contracting a disease. Each decision support tool uses a dynamic programming algorithm to construct recommended immunization schedules in an optimized manner. These tools simplify the tedious process of manually constructing immunization schedules, expedite the process, and eliminate errors.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".