Recording Practices and Satisfaction of Hemophiliac Patients Using Two Different Data Entry Systems
Bibliographic record
Abstract
Record keeping is integral to home treatment for hemophilia. Identified problems with paper diaries include suboptimal compliance and questionable data validity and quality. The effects of an electronic data recording system, Advoy, on data quality, patient adherence, and satisfaction were examined. An exploratory approach was used to examine the sequential use of paper diaries and e-diaries by 38 patients. Data were obtained from paper records for the 6 months preceding the introduction of the electronic record and from the first 6 months of use of Advoy. Completion of mandatory and additional treatment details was also compared. More mandatory information (27.57%) was recorded with the e-diary. As well, the amount of completed additional fields nearly doubled (19.9%-36.5%). Patients tended to complete a greater variety of additional fields with the e-diary than with paper records. Finally, a higher percentage of survey respondents (29.4%) indicated that they were "very satisfied" with Advoy compared with paper records (6.7%). Most survey respondents (94.4%) had a previous experience with electronic programs. The use of the e-diary significantly improved patient adherence in recording mandatory treatment information; the increase in additional data provided by the patients was also found to be an added benefit of this technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".