The ACNE‐Q
Bibliographic record
Abstract
Acne is a common skin condition that affects many adolescents and adults. This study, from Canada and the USA, aimed to create a patient‐reported outcome measure (i.e., questionnaire) called the ACNE‐Q. Patient‐reported outcome measures have many uses, for example they can be used before and after a treatment to assess any improvement in a patient's symptoms, or their quality of care. The ACNE‐Q measures outcomes that matter to people with acne. To develop the ACNE‐Q, the authors interviewed 21 people with acne and acne scars to identify their concerns. From this information, the authors drafted seven scales that measure appearance (of facial, chest and back acne, acne scars and facial skin), symptoms and appearance‐related distress. The scales were shown to 10 patients and 16 experts to make sure they were easy to understand and covered the issues that matter the most to patients. The authors then tested the ACNE‐Q in a sample of 256 patients with acne and/or acne scars. The research team found that the seven scales worked well (were reliable and valid). The ACNE‐Q can now be used in research to measure change in appearance, symptoms and distress following treatment for acne and/or acne scars. The ACNE‐Q can also be used by healthcare providers with their patients to identify concerns, and in shared decision‐making.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".