What's in a Name? The Incorrect Use of Case Series as a Study Design Label in Studies Involving Dogs and Cats
Bibliographic record
Abstract
BACKGROUND: Study design labels are used to identify relevant literature to address specific clinical and research questions and to aid in evaluating the evidentiary value of research. Evidence from the human healthcare literature indicates that the label "case series" may be used inconsistently and inappropriately. OBJECTIVE: Our primary objective was to determine the proportion of studies in the canine and feline veterinary literature labeled as case series that actually corresponded to descriptive cohort studies, population-based cohort studies, or other study designs. Our secondary objective was to identify the proportion of case series in which potentially inappropriate inferential statements were made. DESIGN: Descriptive evaluation of published literature. PARTICIPANTS: One-hundred published studies (from 19 journals) labeled as case series. METHODS: Studies were identified by a structured literature search, with random selection of 100 studies from the relevant citations. Two reviewers independently characterized each study, with disagreements resolved by consensus. RESULTS: Of the 100 studies, 16 were case series. The remaining studies were descriptive cohort studies (35), population-based cohort studies (36), or other observational or experimental study designs (13). Almost half (48.8%) of the case series or descriptive cohort studies, with no control group and no formal statistical analysis, included inferential statements about the efficacy of treatment or statistical significance of potential risk factors. CONCLUSIONS: Authors, peer-reviewers, and editors should carefully consider the design elements of a study to accurately identify and label the study design. Doing so will facilitate an understanding of the evidentiary value of the results.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.493 | 0.814 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".