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A review of research related to blood transfusion in Canada, 2000–2002

2004· review· en· W1978780842 on OpenAlexafffundabout
Jerome Teitel, Pierre Robillard, G. Rock, Durhane Wong‐Rieger, Ecs Lai, PK Chan

Bibliographic record

VenueTransfusion Medicine · 2004
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsMcGill UniversityUniversity of OttawaOttawa HospitalInstitut National de Santé Publique du QuébecUniversity of TorontoSt. Michael's Hospital
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsBlood transfusionTransfusion medicineProductivityPolitical scienceMedicineGrant fundingEconomic growthPublic administrationEconomics

Abstract

fetched live from OpenAlex

The former National Blood Safety Council undertook a comprehensive review of blood transfusion research in Canada for the years 2000 through 2002. Data were acquired by direct contact with agencies which support such research and by searches of the relevant websites. Total grant support increased markedly over the 3-year period, from 4.1 million dollars to 18.5 million dollars. Publicly funded granting agencies, biopharmaceutical companies, the blood services and the province of Ontario were major supporters. Much smaller amounts were granted from charitable organizations. Clinical research attracted the majority of the funding, although a larger number of projects were basic science in nature. Most research was carried out in the provinces of Ontario, Québec and British Columbia. Although we have not assessed the productivity of blood-related research, it appears that substantial amounts of funding were allocated to these projects between 2000 and 2002. These data may provide a helpful perspective to investigators in transfusion medicine elsewhere, who may also be assessing the relative priority given to this field of research in their own countries.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.008
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.067
GPT teacher head0.359
Teacher spread0.292 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2004
Admission routes3
Has abstractyes

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