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Record W1986879066 · doi:10.5326/jaaha-ms-5917

Comparison of Gravity Collection Versus Suction Collection for Transfusion Purposes in Dogs

2013· article· en· W1986879066 on OpenAlexaff
Bérénice Conversy, Marie‐Claude Blais, Lisa Carioto, Julie Beaudoin

Bibliographic record

VenueJournal of the American Animal Hospital Association · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsCentre hospitalier universitaire de QuébecCegep de Saint Hyacinthe
Fundersnot available
KeywordsMedicineHematocritBlood collectionHemolysisAnesthesiaSuctionHeart rateSurgeryDonationHematomaBlood pressureInternal medicineEmergency medicine

Abstract

fetched live from OpenAlex

Blood donation is an essential step in transfusion medicine that must take into account the donor's welfare, collection effectiveness, and blood product quality. This prospective study enrolled 13 canine blood donors, each subjected to both gravity and suction collection methods, in a randomized order. Clinical parameters, including heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), and rectal temperature (RT), were evaluated at four time points, including when the donor was on the floor and on the collection table, and before and after blood donation. The number of times the donor and needle required repositioning, the duration of the donation, the noise created by the apparatus, and the presence of a hematoma were evaluated. The weight, index of hemolysis, and hematocrit of each unit of blood were recorded. There was no significant difference between collection methods for either the clinical parameters at each time point or the prevalence of hematoma formation, the frequency of needle repositioning, the hemolysis index, or hematocrit. Collection by suction was noisier (P < 0.0001), faster (P = 0.004), and associated with significantly less donor repositioning (P = 0.007). Suction appears to be a safe and cost-effective method that should be considered to optimize blood donation.

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)0.001
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 score0.024
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

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