The haemtrack home therapy reporting system: Design, implementation, strengths and weaknesses: A report from UK Haemophilia Centre Doctors Organisation
Bibliographic record
Abstract
INTRODUCTION: Haemtrack is an electronic home treatment diary for patients with inherited bleeding disorders, introduced in 2008. It aimed to improve the timeliness and completeness of patient-reported treatment records, to facilitate analysis of treatment and outcome trends. The system is easy to use, responsive and accessible. METHODS: The software uses Microsoft technologies with a SQL Server database and an ASP.net website front-end, running on personal computers, android and I-phones. Haemtrack interfaces with the UK Haemophilia Centre Information System and the National Haemophilia Database (NHD). Data are validated locally by Haemophilia Centres and centrally by NHD. Data collected include as follows: treatment brand, dose and batch number, time/date of bleed onset and drug administration, reasons for treatment (prophylaxis, bleed, follow-up), bleed site, severity, pain-score and outcome. RESULTS: Haemtrack was used by 90% of haemophilia treatment centres (HTCs) in 2015, registering 2683 patients using home therapy of whom 1923 used Haemtrack, entering >17 000 treatments per month. This included 68% of all UK patients with severe haemophilia A. Reporting compliance varied and 55% of patients reported ≥75% of potential usage. Centres had a median 78% compliance overall. A strategy for progressively improving compliance is in place. Age distribution and treatment intensity were similar in Haemtrack users/non-users with severe haemophilia treated prophylactically. CONCLUSION: The Haemtrack system is a valuable tool that may improve treatment compliance and optimize treatment regimen. Analysis of national treatment trends and large-scale longitudinal, within-patient analysis of changes in regimen and/or product will provide valuable insights that will guide future 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 | 0.045 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".