Evaluation of Glutamine Utilization in Patients With Sickle Cell Disease
Bibliographic record
Abstract
Glutamine (Gln) was FDA-approved in 2017 to reduce acute sickle cell disease (SCD) pain and acute chest syndrome. However, typical pediatric patients with SCD exhibit moderate adherence, measured by a medication possession ratio <80%. This study examined Gln utilization in a "real-world" clinical setting to determine factors influencing medication adherence and to characterize the impact of an interdisciplinary team approach at an institution with specialty pharmacy services. A retrospective chart review identified 40 patients prescribed Gln by sickle cell specialists over a 2-year period and met selection criteria. Gln medication possession ratio for pediatric (72%) and adult (76%) patients were higher than other SCD medications. Pediatric patients (74%) demonstrated significantly lower first-attempt insurance approval rate compared with adult patients (95%) ( P =0.0026), suggesting an initial access barrier for pediatric patients. Pediatric patients demonstrated significantly higher number of medication fills (9.11 fills) compared with adult patients (3.86 fills) ( P =0.007), which suggests interdisciplinary collaboration may facilitate sustainable management of a new therapy. The majority of pediatric (89%) and adult (90%) patients reported high satisfaction with Gln ("excellent") with minor or no side effects. Multidisciplinary health care provider collaborations and tracking medication adherence metrics can help address barriers to care for SCD patients.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".