Are We Appropriately Using Intravenous Immunoglobulin In The Saskatoon Health Region?
Bibliographic record
Abstract
Intravenous Immunoglobulin (IVIG) is a plasma protein used in the treatment of various diseases. The cost of IVIG is approximately $50-$80 per gram. In 2005, Saskatchewan's usage of IVIG was above the national average, at 100g per 1000 persons compared to 95g per 1000 persons. IVIG is currently licensed by Health Canada for use in six diseases; however, IVIG is often used for off-label indications. There are several published guidelines that outline recommended uses of IVIG. These are largely based on case studies and expert opinion. Despite these guidelines, IVIG is often used in multiple settings where there is no supporting evidence. We aimed to evaluate adherence to published guidelines by quantifying IVIG usage in the Saskatoon Health Region (SHR). We conducted a retrospective chart review of patients who received IVIG in 2011 and 2012, classifying the usage based on diagnosis and prescribing specialty. Our primary endpoint was the percentage of appropriate use of IVIG in comparison to published guidelines. Our secondary endpoint was adverse reactions seen in patients receiving IVIG. In 2011, 227 patients received IVIG totalling 50,528g and 2.78 million dollars. In 2012, 216 patients received IVIG totalling 63,155g and 3.49 million dollars. According to published guidelines, 36% of the total usage in 2011 and 21% of the total usage in 2012 was not indicated (Figure 1). IVIG was prescribed the most by neurologists, hematologists, and rheumatologists (Figure 2). Despite the recent guidelines, the results show that there was no significant improvement in usage of IVIG between 2011 and 2012 (p-value 0.079, odds ratio 1.46). No adverse reactions were documented. In conclusion, our data analysis shows that IVIG usage in the SHR does not adhere well to guidelines. Pre-printed order sets outlining appropriate indications for IVIG are currently being introduced in the SHR to help improve adherence to guidelines. After the order sets have been implemented for one year, we will re-evaluate the usage of IVIG in the SHR to see if improvements have been made. Our goal is to ensure there is appropriate resource allocation of IVIG and to reduce risks to the patient and financial costs to the health care system. Disclosures: No relevant conflicts of interest to declare.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".