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Splenectomy as an adjunctive treatment for dogs with immune‐mediated hemolytic anemia: ten cases (2003–2006)

2009· article· en· W2080151662 on OpenAlexfundno aff
Jason E. Horgan, Brian K. Roberts, Thomas Schermerhorn

Bibliographic record

VenueJournal of Veterinary Emergency and Critical Care · 2009
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsnot available
FundersInstitute of Musculoskeletal Health and Arthritis
KeywordsMedicineSplenectomyMedical recordAnemiaPopulationHemolytic anemiaRetrospective cohort studyPediatricsSurgeryInternal medicineSpleen

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the patient population, disease severity, and outcome in dogs with immune-mediated hemolytic anemia (IMHA) that underwent splenectomy. To compare presurgical and postsurgical data. DESIGN: Retrospective case series. SETTING: Emergency clinic/referral hospital. ANIMALS: Ten dogs diagnosed with IMHA. INTERVENTIONS: Splenectomy in addition to standard medical management for IMHA. MEASUREMENTS: Medical records of 10 dogs with IMHA, in which a splenectomy was performed were reviewed. The population was analyzed with regards to physical and clinicopathologic data, severity, treatment, and outcome. Outcome was defined as survival at 30 days, percentage of dogs on medications at 30 days, and number of relapses documented by 30 days. The presurgical and postsurgical PCV and transfusion requirements were documented and compared for each dog. RESULTS: Nine of 10 dogs survived to 30 days. Four of the 9 that survived were not on any immunosuppressive medications. There were no relapses during the 30 days. The 3-day postsplenectomy PCVs were significantly higher than presplenectomy. The number of transfusions administered postsplenectomy was significantly less than those administered presplenectomy. CONCLUSION: The use of splenectomy may be associated with an improved outcome in dogs with IMHA.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.029
GPT teacher head0.335
Teacher spread0.306 · 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 designNon-randomized trial
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

Citations30
Published2009
Admission routes1
Has abstractyes

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