Treatment of Acne Vulgaris and Prevention of Acne Scarring: Canadian Consensus Guidelines
Bibliographic record
Abstract
Acne affects approximately 95% of the population at some point during their lifetime.1 This common disorder can range from mild to severe forms, cause sometimes extensive scarring, and can last well into the fourth and fifth decades. Effective therapeutic agents are available to both treat acne and prevent ongoing disease. Despite this, dermatologists frequently see patients with significant acne scarring because many patients delay seeking medical attention for acne and many practitioners procrastinate over using effective antiscarring options. In patients who already demonstrate scarring, repeated courses of antibiotics only result in recurring acne and additional scarring. This, in turn, exacerbates the despair and other adverse psychosocial effects of the disease. There are a variety of agents and devices to help acne patients with scarring. However, successful treatment cannot be guaranteed, and in most cases residual scarring will be evident. Thus, the most effective way of managing acne scarring is to prevent its occurrence in the first place. Although we currently have a number of effective antiacne agents to control the disease, such as antibiotics and hormonal agents, isotretinoina is the only agent that has been shown to induce long-term drug-free remission and curative potential.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".