Using blood wisely in oncology patients: An institutional analysis of the Choosing Wisely Canada restrictive transfusion strategy guidelines.
Bibliographic record
Abstract
10 Background: Numerous studies support the safety of single-unit red red blood cell (RBC) transfusions and restrictive pre-transfusion hemoglobin (Hg) thresholds (≤ 70-80 g/L) among diverse populations of hospitalized patients, including those with malignancies on myelosuppressive systemic therapy. Using Blood Wisely is a national Choosing Wisely Canada (CWC) initiative that challenges hospitals to benchmark themselves on evidence-informed restrictive transfusion strategies, with the aim to decrease inappropriate RBC transfusions in Canada. We assessed transfusion practices among oncology inpatients at BC Cancer Vancouver Centre as part of this initiative and performed an exploratory analysis among outpatients. Methods: BC Cancer Vancouver RBC transfusion records were obtained for the period of October 2019-September 2020. The percentage of single-unit transfusions and transfusions for Hg ≤ 80 g/L were measured, to assess adherence to CWC targets of ≥ 65% and ≥ 80%, respectively, for these metrics. Univariate analyses were used to compare treatment variables and transfusion outcomes. Results: During the 1-year audit period, 120 inpatient and 586 outpatient RBC transfusions occurred. For inpatient transfusions, 40.8% (n = 49) were single-unit and 79.2% (n = 95) were for Hg ≤ 80 g/L. For outpatient transfusions, 11.8% (n = 69) were single-unit and 65.7% (n = 304) were for Hg ≤ 80 g/L. Outpatients and patients with solid malignancies were more likely to receive multiple-unit transfusions, compared to inpatients (p < 0.001) and patients with hematological malignancies (p < 0.001), respectively. Patients with solid malignancies and those on active treatment were more likely to be transfused when Hg was > 80 g/L, compared to patients with hematological malignancies (p = 0.004) and those off treatment (p = 0.003), respectively. Multiple RBC units were more likely to transfused when the interval from pre-transfusion bloodwork to receipt of transfusion was > 3 days (p = 0.029). Conclusions: A high rate of inappropriate RBC transfusions are being ordered for oncology patients at our institution, in discordance with CWC restrictive transfusion strategy guidelines. Quality improvement interventions are planned to adopt best practices in transfusion medicine in this high-use population.[Table: see text]
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".