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Record W2742625797 · doi:10.1136/vetreccr-2017-000497

Tip of an iceberg: complications of an oesophageal foreign body removal in a dog

2017· article· en· W2742625797 on OpenAlexaboutno aff
Tristan Merlin, Carol Hoy, Diego Rodrigo

Bibliographic record

VenueVeterinary Record Case Reports · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsnot available
FundersDivision of Electrical, Communications and Cyber Systems
KeywordsMedicineRespiratory distressForeign bodyPneumoniaMechanical ventilationIntensive care unitForeign Body RemovalAspiration pneumoniaAnesthesiaHypovolemiaLabrador RetrieverSurgeryGeneral anaesthesiaVentilation (architecture)Physical examinationIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

A female golden retriever dog was presented for the removal of an oesophageal foreign body. Clinical examination on admission revealed a mild hypovolemia associated with tachypnoea. Radiographic examination of the chest did not reveal any other condition apart from the presence of two oesophageal foreign bodies. The animal was anaesthetised to attempt an endoscopic removal, and after induction of anaesthesia a severe desaturation and difficulties providing manual ventilation were noted. These signs persisted during the entire procedure, and after recovery from anaesthesia a severe aspiration pneumonia was diagnosed. The animal’s condition worsened quickly and required the use of long‐term mechanical ventilation in the intensive care unit. After 3 days of hospitalisation, the animal was euthanased due to the development of an acute respiratory distress syndrome and the lack of clinical improvement.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0100.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.092
GPT teacher head0.388
Teacher spread0.295 · 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 designCase report
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
Published2017
Admission routes1
Has abstractyes

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