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Record W4409732742 · doi:10.1111/vco.13061

Neurological Recovery in 14 Cats With Epidural Lymphoma Treated With Chemotherapy

2025· article· en· W4409732742 on OpenAlexaff
Julya Nathalya Felix Chaves, Mathias Reginatto Wrzesinski, Júlia da Silva Rauber, Dênis Antonio Ferrarin, Marcelo Luís Schwab, Glaucia D. Kommers, Mariana Martins Flores, Ana Paula da Silva, Angel Ripplinger, Diego Vilibaldo Beckmann, Alexandre Mazzantí

Bibliographic record

VenueVeterinary and Comparative Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsCanadian Veterinary Medical Association
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMedicineCATSLymphomaAnesthesiaChemotherapyAmbulatoryTetraparesisParaplegiaSurgerySpinal cordInternal medicineRadiologyMagnetic resonance imaging

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the rate and timing of neurological recovery in cats with epidural lymphoma who were treated with chemotherapy. The study included cats with various degrees of neurological impairment, confirmed diagnosis of lymphoma and spinal cord involvement. At the start of treatment, of the 14 cats diagnosed with lymphoma, 14.3% (n = 2) had ambulatory paraparesis, 14.3% (n = 2) had non-ambulatory paraparesis, 7.1% (n = 1) paraplegia with deep nociception, 50% (n = 7) were paraplegic with absent deep nociception and 14.3% (n = 2) had non-ambulatory tetraparesis. The chemotherapy treatment given was COP in 10 cats, COP and CHOP in 2 cats and CHOP in 2 cats. The number of chemotherapy sessions needed for neurological recovery varied from 1 to 4, with a total of 1-13 sessions per cat. The rate of neurological recovery was satisfactory in 83.3% (10/12) of the cats. This study indicates that cats with epidural lymphoma treated with chemotherapy have an 83.3% likelihood of partial neurological recovery and a 50% chance of full neurological recovery within a period of 7-28 days, regardless of the level of neurological impairment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.089
GPT teacher head0.388
Teacher spread0.299 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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