Reporting the level of evidence in the Canadian Journal of Plastic Surgery: Why is it important?
Bibliographic record
Abstract
I n 2007, the British Medical Journal polled its members to list the top 15 advances in medicine over the past 150 years.Among the top 15 were the introduction of sanitation, antibiotics and vaccines, as well as evidence-based medicine (EBM) (1).The definition of EBM can be summarized as the integration of best research evidence with clinical expertise and patient values (2).EBM also considers the setting, circumstances and available resources (3). Figure 1 presents the variables that we as surgeons need to consider when making treatment decisions for our patients.Unfortunately, only a minority of plastic surgeons incorporate EBM in their daily practice.One may ask, so what?Generally speaking, if we do not adopt EBM, we would be ignoring one of medicine's most important advances.Regardless of specialty, no surgeon would suggest withholding antibiotics in the presence of an established infection, or not adhering to water sanitation practices.We would hope the above analogy makes the point clear.On a practical note, if each of us performs interventions in our daily practices without considering the best available evidence, we may be offering our patients inferior treatments and/or squandering scarce health care resources.Cumulatively, it has been estimated that 25% of health care dollars are wasted in Canada (4).This is comparable with reports of up to 50% of health care money wasted in the United States (5), and 20% to 40% worldwide (6). HISTORY OF EBM AND THE LEVELS OF EVIDENCEThe term 'EBM' was coined in 1990 by Dr Gordon Guyatt, an internist and epidemiologist, during his tenure as Program Director of McMaster University's Internal Medicine Program (Hamilton, Ontario) (7).The origins of the EBM movement, however, can be credited to Dr David Sackett.He is the 'founding father' of the first Department of Clinical Epidemiology & Biostatistics, established in 1966 as part of McMaster's new School of Medicine.During the late 1970s, Dr Sackett and a group of clinical epidemiologists created a series of articles advising clinicians on how to read clinical journals.The series appeared in the Canadian Medical Association Journal in 1981 (3).
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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 | 0.342 | 0.771 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".