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Record W3024884613

Do plasma protein:fibrinogen ratios in horses provide additional information compared with fibrinogen concentration alone?

2019· article· en· W3024884613 on OpenAlexaff
Nicole Fernandez, Marie‐France Roy

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFibrinogenAlbuminInflammationInternal medicineBlood proteinsMedicineEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

The plasma protein:fibrinogen (PP:F) ratio was introduced to aid interpretation of hyperfibrinogenemia by accounting for dehydration. However, this ratio is inconsistently assessed in practice and its clinical value remains unknown. Our objective was to determine whether the PP:F ratio provides additional information in adult horses beyond fibrinogen concentration alone. Two databases were reviewed to identify 412 hyperfibrinogenemic horses. Plasma protein:fibrinogen ratios were calculated and their interpretation compared to the fibrinogen concentration. Ratios < 15 were supportive of inflammation. Albumin and total protein concentrations were evaluated when ratios were ≥ 15 to determine if inflammation was supported. Very good agreement (86%) was found on the presence of inflammation when PP:F ratios were compared to fibrinogen concentration. In 72% of cases in which PP:F ratios did not support inflammation, inflammation was considered likely based on albumin and total protein. These findings suggest that PP:F ratios do not provide additional information in horses over fibrinogen concentrations alone.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0130.007
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.280
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2019
Admission routes1
Has abstractyes

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