Reducing the burden of iron deficiency in cystic fibrosis using a clinic-based protocol: A quality improvement initiative
Bibliographic record
Abstract
Introduction: Iron deficiency (ID) and iron deficiency anemia (IDA) are common complications in people with cystic fibrosis (pwCF). While cystic fibrosis transmembrane receptor (CFTR) modulator therapy, specifically elexacaftor-tezacaftor-ivacaftor (ETI), has been shown to improve nutritional status and iron absorption, ID and IDA remain prevalent. This project evaluates the effectiveness of an iron therapy protocol (ITP) in reducing ID and IDA in pwCF. Methods: A retrospective chart review was conducted for 169 patients followed at an adult cystic fibrosis clinic serving the maritime provinces in Canada. Prevalence of ID and IDA was assessed in March 2022 and again in April 2024 following the implementation of an ITP in mid-March 2022. ID was defined by ferritin, serum iron, and transferrin saturation levels, while IDA was defined as ID in the presence of low hemoglobin. Treatment included oral and intravenous iron supplementation. An assessment for celiac disease was performed to ensure other causes of malabsorption were ruled out. Results: In March 2022, ID was present in 27% and IDA in 6% of patients assessed. In April 2024, post-ITP implementation, the prevalence of ID decreased to 5% and IDA to 3%. ETI was introduced prior to protocol initiation, and no significant changes in ID or IDA were seen until the protocol was initiated. Discussion: The clinic protocol significantly reduced the prevalence of ID and IDA in this quality improvement project. ETI may enhance nutritional status, among other benefits, but an organized approach to diagnosis and treatment of ID and IDA remains essential for managing iron-related complications in pwCF.
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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 | 0.064 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".