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Serum Alpha 1-Acid Glycoprotein Concentrations in Healthy and Tumor-Bearing Cats

2000· article· en· W2054995079 on OpenAlexaff
Kim A. Selting, Gregory K. Ogilvie, Susan E. Lana, M. J. Fettman, Kathy L. Mitchener, Rodney A. Hansen, Kristi L. Richardson, Judy A. Walton, Margie Scherk

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsSpinal Cord Injury BC
Fundersnot available
KeywordsMedicineCATSGlycoproteinOrosomucoidAlpha (finance)Internal medicineEndocrinologyMolecular biologySurgery

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate alpha 1-acid glycoprotein (AGP) concentrations in tumor-bearing and healthy cats. The hypothesis of the present study was that AGP concentrations would be significantly increased in tumor-bearing cats. Serum from 51 healthy and 97 tumor-bearing, client-owned cats was harvested at the time of presentation and stored at -80 degrees C until assayed. Cats with measurable, histologically confirmed malignancies, and healthy cats of similar ages were included. Serum was assayed for AGP concentration by using a radial immunodiffusion method. AGP concentrations were significantly (P = .0051) higher in tumor-bearing (763 +/- 595 microg/mL; mean +/- SD) when compared to healthy cats (501 +/- 377 microg/mL; mean +/- SD). Of the tumor-bearing cats, 35 had carcinomas, 33 had sarcomas, and 26 had discrete, round cell tumors. AGP concentrations were 645 +/- 62 microg/mL, 660 +/- 540 microg/mL, and 967 +/- 860 microg/mL, respectively, and there were no significant differences among the groups.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.395
Teacher spread0.335 · 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

Citations44
Published2000
Admission routes1
Has abstractyes

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