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Orchiectomy for testicular hypertrophy in an elderly canine: Clinical outcomes and considerations

2024· article· en· W4404073915 on OpenAlexaboutno aff
Deepti Netam, J K Tandon, Muskan Sengar, Alok Kumar Jaiswal, B. NERSTR M, Raju Sharda

Bibliographic record

VenueInternational Journal of Advanced Biochemistry Research · 2024
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsOrchiectomyMuscle hypertrophyMedicineUrologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

This case report describes the successful surgical management of a geriatric dog presented with testicular hypertrophy secondary to testicular torsion and tumor, accompanied by markedly elevated packed cell volume (PCV) and haemoglobin (Hb) levels. The patient, a 17-year-old male Labrador retriever dog, showed clinical signs of pain and discomfort. Diagnostic blood work revealed a PCV of 75% and Hb of 22 g/dL, complicating the anaesthetic and surgical plan. Preoperative fluid therapy was initiated to stabilize the dog, followed by an orchiectomy. This report discusses the challenges encountered, surgical procedure, and postoperative outcomes, highlighting the importance of careful pre-surgical planning and tailored anaesthetic management in geriatric patient.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.083
GPT teacher head0.505
Teacher spread0.422 · 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
Published2024
Admission routes1
Has abstractyes

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