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Peer Review #1 of "Clinical applicability of the Feline Grimace Scale: real-time versus image scoring and the influence of sedation and surgery (v0.2)"

2020· peer-review· en· W4250620345 on OpenAlexaff
Marina C. Evangelista, Javier Benito, Beatriz P. Monteiro, Ryota Watanabe, Graeme M. Doodnaught, Daniel Pang, Paulo V. Steagall

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of CalgaryUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsSedationScale (ratio)MedicineAnesthesiaCartographyGeography

Abstract

fetched live from OpenAlex

Background.The Feline Grimace Scale (FGS) is a facial expression-based scoring system for acute pain assessment in cats with reported validity using image assessment.The aims of this study were to investigate the clinical applicability of the FGS in real-time when compared with image assessment, and to evaluate the influence of sedation and surgery on FGS scores in cats.Methods.Sixty-five female cats (age: 1.37 ± 0.9 years and body weight: 2.85 ± 0.76 kg) were included in a prospective, randomized, clinical trial.Cats were sedated with intramuscular acepromazine and buprenorphine.Following induction with propofol, anesthesia was maintained with isoflurane and cats underwent ovariohysterectomy (OVH).Pain was evaluated at baseline, 15 minutes after sedation, and at 0.5, 1, 2, 3, 4, 6, 8, 12 and 24 hours after extubation using the FGS in real-time (FGS-RT).Cats were video-recorded simultaneously at baseline, 15 minutes after sedation, and at 2, 6, 12, and 24 hours after extubation for subsequent image assessment (FGS-IMG), which was performed six months later by the same observer.The agreement between FGS-RT and FGS-IMG scores was calculated using the Bland & Altman method for repeated measures.The effects of sedation (baseline versus 15 minutes) and OVH (baseline versus 24 hours) were assessed using linear mixed models.Responsiveness to the administration of rescue analgesia (FGS scores before versus one hour after) was assessed using paired ttests.Results.Minimal bias (-0.057) and narrow limits of agreement (-0.351 to 0.237) were observed between the FGS-IMG and FGS-RT.Scores at baseline (FGS-RT: 0.16 ± 0.13 and FGS-IMG: 0.14 ± 0.13) were not different after sedation (FGS-RT: 0.2 ± 0.15, p = 0.39 and FGS-IMG: 0.16 ± 0.15, p = 0.99) nor at 24 hours after extubation (FGS-RT: 0.16 ± 0.12, p = 0.99 and FGS-IMG: 0.12 ± 0.12, p = 0.96).Thirteen cats required rescue analgesia; their FGS scores were lower one hour after analgesic administration (FGS-RT: 0.21 ± 0.18 and FGS-IMG: 0.18 ± 0.17) than before (FGS-RT: 0.47 ± 0.24, p = 0.0005 and

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)0.023
metaresearch head score (Gemma)0.184
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.184
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.002
Science and technology studies0.0040.002
Scholarly communication0.0090.004
Open science0.0040.005
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.2160.104

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 head0.063
GPT teacher head0.430
Teacher spread0.368 · 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.

Study designNot applicable
DomainEvaluation
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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