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Record W4256114464 · doi:10.1111/jvim.12354

Letter to the Editor

2014· letter· en· W4256114464 on OpenAlexaff
Beatriz P. Monteiro, Paulo V. Steagall, B. Duncan X. Lascelles

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2014
Typeletter
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

The authors would like to thank Dr. Mathews and Dr. Dyson for their comments on our recently published article "Systematic Review of Nonsteroidal Anti-Inflammatory Drug-Induced Adverse Effects in Dogs", as it contributes to further discussion on the topic. Indeed, we acknowledge that the article by Mathews et al. 1990 "Nephrotoxicity in dogs associated with methoxyflurane anesthesia and flunixin meglumine analgesia" was erroneously referenced beside the statement "a nonsteroidal anti-inflammatory drug (NSAID) was administered with a corticosteroid or another NSAID". In fact, an erratum had already been submitted to correct this reference positioning error. Our study attempted to identify and evaluate the quality of information regarding NSAID-induced adverse effects in dogs through a systematic review. It is important to remember that our approach used a well-recognized means of assessing multiple studies in a fair and meaningful manner. The assessment criteria are clearly outlined in Tables 1, 2 and 3 and represent a means of evaluating information regarding single NSAIDs, or NSAIDs as a group, in a way that can be meaningfully applied to the population of dogs being treated in practice. Essentially, this is a way to grade the 'generalizability' of clinical and experimental research to the clinical population. Additionally, our review focused on distilling the available information on safety associated with the drug itself, not varying combinations of NSAIDs and other agents. We did not discuss the merit, nor the temporal or historical context of each individual study which would be beyond the scope of this review, and potentially misleading unless each and every study was discussed in that manner. We do realize the importance of the study by Mathews et al. 1990 at that time, and complement them for providing scientific evidence that methoxyflurane (MOF) in association with flunixin meglumine (FM) led to acute renal failure in dogs. However, despite its importance in indicating a link between the concurrent use of MOF and an NSAID (FM), that study, either alone, or in combination with the other available evidence regarding FM, does not constitute a substantial body of information regarding FM-induced adverse effects. Further, when looking across different NSAIDs, there is a dearth of information about NSAID-induced renal adverse effects. Indeed, in conclusion, we suggested "Large clinical trials associated with better specific diagnostic tools may elucidate the incidence of renal adverse drug experience in dogs after NSAID administration." With respect, we stand by our conclusion of "extremely low strength of evidence" for flunixin meglumine, but this in no way detracts from the importance of the Mathews et al. study in communicating a potential issue associated with the concurrent use of MOF and flunixin meglumine.

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)0.005
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0050.006
Open science0.0030.003
Research integrity0.0190.018
Insufficient payload (model declined to judge)0.0540.029

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 head0.271
GPT teacher head0.511
Teacher spread0.240 · 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
Domainnot available
GenreEditorial

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
Published2014
Admission routes1
Has abstractyes

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