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Operating room blood delivery turnaround time: a College of American Pathologists Q-Probe Study of 12647 units of blood components in 466 institutions.

2002· article· en· W61372540 on OpenAlexaboutno aff
David A. Novis, Richard Friedberg, Stephen W. Renner, Frederick A. Meier, Molly K. Walsh

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsnot available
Fundersnot available
KeywordsBlood componentBlood bankMedicineTurnaround timeBlood transfusionEmergency medicineSurgeryOperations management

Abstract

fetched live from OpenAlex

Objectives To determine the normative distribution of time elapsed for blood bank personnel to fill nonscheduled operating room (OR) blood component orders in hospital communities throughout the United States, and to examine hospital blood bank practices associated with faster blood component delivery times. Design Participants in the College of American Pathologists Q-Probes laboratory quality improvement program collected data prospectively on the times elapsed for blood bank personnel to fill nonscheduled emergent orders from hospital ORs for red blood cell (RBC) products, fresh frozen plasma (FFP), and platelets (PLTs). Participants also completed questionnaires describing their hospitals' and blood banks' laboratory and transfusion practices. Setting and participants Four hundred sixty-six public and private institutions located in 48 states in the United States (n = 444), Canada (n = 9), Australia (n = 8), the United Kingdom (n = 4), and Spain (n = 1). Main outcome measures The median time elapsed between requests for blood components by OR personnel and the retrieval of those components by blood component transport personnel, and the median time elapsed between requests for blood components by OR personnel and the arrival of those components in ORs. Results Participants submitted data on 12 647 units of RBCs, FFP, and PLTs. The median aggregate request-to-retrieval turnaround times (TATs) for RBCs, FFP, and PLTs ranged from 30 to 35 minutes, and the median aggregate request-to-arrival TATs for RBCs, FFP, and PLTs ranged from 33 to 39 minutes. Most of the TAT was consumed by events occurring prior to, rather than after release of components from blood banks. Shorter prerelease TATs were associated with having surgical schedules that listed patients' names and procedures available to blood bank personnel prior to surgeries, and having adequate clotted specimens in the blood bank and completed type-and-screen procedures performed before requests for blood components were submitted to blood banks. Among the fastest-performing 10% of participants (90th percentile and above), request-to-retrieval TATs ranged from 12 to 24 minutes for the 3 blood components, whereas among the slowest-performing 10% of participants (10th percentile and below), request-to-retrieval TATs ranged from 63 to 115 minutes for the 3 components. Median TATs ranged from 33 to 37 minutes for the 3 components. Institutions with TATs in the fastest-performing 25th percentile more frequently stored cross-matched RBCs in the OR daily, stocked PLTs for unexpected surgical use, stored PLTs in or near the OR, and had laboratory rather than nonlaboratory personnel deliver components to the OR than did those institutions with TATs in the slowest-performing 25th percentile. Conclusions Hospital blood bank personnel can deliver blood components to the OR in slightly longer than 30 minutes, measured from the time that those units are requested by OR personnel. Practices aimed at saving time before components are released from blood banks will be more efficient in reducing overall TAT than those practices aimed at saving time after components are released from blood banks. Specific practices associated with shorter blood delivery TATs included providing blood bank personnel with access to the names of surgical patients potentially requiring blood components, having pretransfusion testing completed on those patients prior to surgery, having ample blood products on hand, and having laboratory personnel control blood product delivery.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.340
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2002
Admission routes1
Has abstractyes

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