Web-based Application for the Population Pharmacokinetic Service (WAPPS)'s impact on dosage selection: a single paediatric centre experience
Bibliographic record
Abstract
Abstract Background Current treatment for severe haemophilia includes prophylactic factor replacement to prevent bleeding. Coagulation factor products have significant inter-patient variability in pharmacokinetic (PK) parameters. Optimal management requires tailoring prophylaxis to individual PK parameters. Web-based Application for the Population Pharmacokinetic Service (WAPPS) is a tool that estimates individual PK values using a population approach. Despite its growing use to help guide dosing selection, few studies have investigated its clinical impact. Aim To investigate any change in prophylaxis regimen and hours per week where factor level is under 1%, pre- and post-PK testing using WAPPS, for paediatric patients with severe haemophilia. Methods A retrospective chart review was conducted for all paediatric patients with severe haemophilia receiving care between April 2013 and July 2018 at McMaster Children's Hospital who have used WAPPS. Data extracted included: patient demographics, PK data generated by WAPPS, prophylaxis regimen pre- and post-PK testing, and reason for regimen change. The number of hours per week where factor level was under 1% pre- and post-PK testing was calculated using WAPPS. Results Thirty-one patients were included; 42% (n=13) changed their prophylaxis regimen after PK testing. After using PK data to personalise prophylaxis recommendations, there was a decrease in the number of hours per week where factor level is under 1% (from an average of 13.1 hours/week to 11.8 hours/week), though not statistically significant (p=0.16). Conclusion PK data generated by WAPPS has direct impact by informing changes to prophylaxis recommendations. This individualised approach promotes patient-centred care and patient engagement without increasing the time spent with factor levels below 1%. It also confirms and validates clinical practice.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".