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Record W2550865025 · doi:10.1093/ajcp/aqw181

Survey of Institutional Policies for Provision of “CMV-Safe” Blood in Ontario

2016· article· en· W2550865025 on OpenAlexaffabout
Laura Finlay, Pria Nippak, James H. Tiessen, Winston Isaac, Jeannie Callum, Christine Cserti‐Gazdewich

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2016
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto Metropolitan UniversityToronto East General HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsCytomegalovirusBusinessLeukoreductionStandardizationMedicineBest practicePopulationBlood transfusionEnvironmental healthFamily medicinePolitical scienceImmunologyHuman immunodeficiency virus (HIV)HerpesviridaeViral disease

Abstract

fetched live from OpenAlex

OBJECTIVES: Debate continues on whether leukoreduction alone (LR) is sufficiently similar to leukoreduced cellular products drawn from cytomegalovirus (CMV)-seronegative (SN) donors to minimize the risk of transfusion-transmitted CMV (TT-CMV). We sought to determine the policy, inventory, and practice landscape of the province for TT-CMV mitigation. METHODS: A web-based survey was distributed to hospitals in Ontario by Canadian Blood Services to collect data on their policies with respect to TT-CMV prevention. RESULTS: TT-CMV mitigation practices varied by patient population, hospital size, and region. Smaller institutions remain committed to dual prevention, whereas academic hospitals favor a single-measure approach. Although smaller institutions attempt to align their policies with leadership sites, emulation is often inaccurate. The demands for SN products also appear to be significantly lower than the current screening practices of Canadian Blood Services. CONCLUSIONS: Standardization is lacking on practices to prevent TT-CMV. Although there are barriers to harmonizing practices, the apparent shift to policies acknowledging LR as a sufficient protection is likely to continue.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.132
GPT teacher head0.448
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2016
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Clinical PathologySame topicCytomegalovirus and herpesvirus researchFrench-language works237,207