A survey of patients with haemophilia to understand how they track product used at home
Bibliographic record
Abstract
Record keeping among individuals who manage haemophilia at home is an essential tool of communication between patient and Haemophilia Treatment Center (HTC). Complete records help HTCs monitor patients, their use of factor and ensure treatment is optimal. HTCs provide patients with a number of methods to track infusion practices. The study objectives were to: [1] determine the current methods of record keeping; [2] identify previous methods of record keeping; [3] understand the strengths and weaknesses associated with each method; and [4] gather suggestions for improvement. Survey methods were used to address the research objectives. Of the 83 patients in the Hamilton-Niagara region who received the survey distributed through the local HTC, 51 returned surveys were included into the analysis. Descriptive statistics were used. Results indicate individuals with haemophilia record infusion practices using: paper diaries, excel spreadsheets, hand-held PDAs and/or the online EZ-Log Web Client. The most popular method of record keeping was EZ-Log (45.1%) followed by paper diaries (35.2%). Advantages to using paper methods include the visual tracking of information and retaining hardcopies. The disadvantage was the inconvenience of physically submitting the records monthly. Advantages to using the online EZ-Log Web Client included ease of use and improved accuracy. The primary disadvantage was technical errors that were difficult to troubleshoot. Record keeping practices among individuals with haemophilia seem to vary according to personal preference and convenience. Respondents suggested that saving infusion history, incorporating barcode scanners or a copy and paste function could improve electronic methods.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".