Reporting the level of evidence in the Canadian Journal of Plastic Surgery: Why is it important?
Bibliographic record
Abstract
The levels of evidence (LOE) table has been increasingly used by many surgical journals and societies to emphasize the importance of proper study design. Since their origin, LOE have evolved to consider multiple study designs and also the rigour of not only the study type but multiple aspects of its design. The use of LOE aids readers in appraising the literature while encouraging clinical researchers to produce high-quality evidence. The current article discusses the benefits and limitations of the LOE, as well as the LOE of articles published in the Canadian Journal of Plastic Surgery (CJPS). Along with an assessment of the LOE in the CJPS, the authors have provided recommendations to improve the quality and readability of articles published in the CJPS.
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 | 0.478 | 0.845 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".