Evidence-based medicine: The Canadian Journal of Plastic Surgery's future
Bibliographic record
Abstract
Over the past few decades, evidence-based medicine has been rated as one of the most significant medical advances in patient care (1). The ability to systematically rate research evidence should direct patient care so that the most beneficial interventions for patients are implemented. In the absence of intervention evaluation, significant portions of our health care dollars continue to be wasted on care that does not benefit the patient or has never been appraised for its supporting evidence. In the Spring 2012 issue of the Canadian Journal of Plastic Surgery (CJPS), the article by Thoma et al (2) provided a brief history and detailed the importance of evidence-based medicine. Currently, more and more scientific journals are focusing on evidence-based medicine and level of evidence (LOE) rating scales when evaluating journal articles. These scales enable researchers and authors to produce better articles, and enable readers to easily understand the LOE for each published article. There are many LOE rating scales in the literature. The CJPS has decided to use the rating system and standardized icons currently used by Plastic and Reconstructive Surgery. The editorial by Sullivan et al (3) highlights the importance of evidence-based medicine and the need for journals to embrace LOE rating scales. The LOE tables to be implemented by the CJPS were developed by the American Society of Plastic Surgeons and are presented here. It is with their full support and encouragement that we begin duplicating their requirements for authors to rate their articles based on the LOE in one of three categories: THERAPEUTIC, DIAGNOSTIC or PROGNOSTIC/RISK. Each article will have its level rated based on the set criteria established by the American Society of Plastic Surgeons. The CJPS’s Editorial Board will then confirm the level and publish the level with the article. The same triangular rating logo will be used in a similar manner to what is currently published in Plastic and Reconstructive Surgery (Figure 1). Figure 1 The level of evidence pyramid, showing level of evidence (I through V) and clinical question addressed by the article. Left Diagnostic clinical question addressed, with a level of evidence of II. Centre Therapeutic clinical question addressed, with a ... Hopefully, this new initiative will enhance the quality of the work published in the CJPS and enhance the educational experience of our readers.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".