A Derivation and Validation Study of an Early Blood Transfusion Needs Score for Severe Trauma Patients
Bibliographic record
Abstract
BACKGROUND: There is no existing adequate blood transfusion needs determination tool that Emergency Medical Services (EMS) personnel can use for prehospital blood transfusion initiation. In this study, a simple and pragmatic prehospital blood transfusion needs scoring system was derived and validated. METHODS: Local trauma registry data were reviewed retrospectively from 2004 through 2013. Patients were randomly assigned to derivation and validation cohorts. Multivariate logistic regression was used to identify the independent approachable risks associated with early blood transfusion needs in the derivation cohort in which a scoring system was derived. Sensitivity, specificity, and area under the receiver operational characteristic (AUC) were calculated and compared using both the derivation and validation data. RESULTS: A total of 24,303 patients were included with 12,151 patients in the derivation and 12,152 patients in the validation cohorts. Age, penetrating injury, heart rate, systolic blood pressure, and Glasgow coma scale (GCS) were risks predictive of early blood transfusion needs. An early blood transfusion needs score was derived. A score > 5 indicated risk of early blood transfusion need with a sensitivity of 83% and a specificity of 80%. A sensitivity of 82% and a specificity of 80% were also found in the validation study and their AUC showed no statistically significant difference (AUC of the derivation = 0.87 versus AUC of the validation = 0.86, P > 0.05). CONCLUSIONS: An early blood transfusion scoring system was derived and internally validated to predict severe trauma patients requiring blood transfusion during prehospital or initial emergency department resuscitation.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".