MétaCan
Menu
Back to cohort
Record W2991473054

Effect of aging on hematological profile of obese dogs

2018· article· en· W2991473054 on OpenAlexaboutno aff
A Abinaya, Karu Pasupathi, R. Karunakaran, Cecilia Joseph, Senthil Nr, S. Vairamuthu

Bibliographic record

VenueInternational Journal of Chemical Studies · 2018
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMean corpuscular hemoglobin concentrationMean corpuscular hemoglobinHematocritHemoglobinMedicineReference rangeBlood countMean corpuscular volumePhysiologyComplete blood countStatistical significanceInternal medicineHematologyVeterinary medicineGastroenterology
DOInot available

Abstract

fetched live from OpenAlex

Fifteen obese Labrador dogs were selected from the animals brought to Madras Veterinary College Teaching Hospital. The animals of either sex were categorized according to their age groups, viz., 3-5 years, 5-8 years and above 8 years. The animals were given complete physical examination and whole blood samples were collected and analysed for the hematological parameters including hemoglobin, hematocrit, RBC count, platelet count, WBC and differential count. The values were compared with the reference range, and the results of our study revealed the normal concentration of hematological parameters and found no statistical significance between the age groups. Though the mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration deviated from the normal range, the age did not influence the other blood parameters.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.372
Teacher spread0.348 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Chemical StudiesSame topicLiver Disease Diagnosis and TreatmentFrench-language works237,207