A systematic review of criteria used to report complications in soft tissue and oncologic surgical clinical research studies in dogs and cats
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To evaluate reporting of surgical complications and other adverse events in clinical research articles describing soft tissue and oncologic surgery in dogs and cats. STUDY DESIGN: Systematic literature review. SAMPLE: English-language articles describing soft tissue and oncologic surgeries in client-owned dogs and cats published in peer-reviewed journals from 2013 to 2016. METHODS: CAB, AGRICOLA, and MEDLINE databases were searched for eligible articles. Article characteristics relevant to complications were abstracted and summarized, including reported events, definitions, criteria used to classify events according to severity and time frame, and relevant citations. RESULTS: One hundred fifty-one articles involving 10 522 animals were included. Canine retrospective case series of dogs predominated. Ninety-two percent of articles mentioned complications in study results, but only 7.3% defined the term complication. Articles commonly described complications according to time frame and severity, but terminology and classification criteria were highly variable, conflicting between studies, or not provided. Most (58%) reported complications could have been graded with a published veterinary adverse event classification scheme, although common intraoperative complications were notable exceptions. CONCLUSION: Definitions and criteria used to classify and report soft tissue and oncologic surgical complications are often absent, incomplete, or contradictory among studies. CLINICAL SIGNIFICANCE: Lack of consistent terminology contributes to inadequate communication of important information about surgical complications. Standardization of terminology and consistency in severity scoring will improve comparative evaluation of clinical research results.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it