Bibliographic record
Abstract
Psoriasis has been associated with a number of behavioral and systemic comorbidities, including psoriatic arthritis, anxiety, depression, obesity, hypertension, diabetes mellitus, hyperlipidemia, metabolic syndrome, smoking, cardiovascular disease, alcoholism, Crohn's disease, lymphoma, and multiple sclerosis. Many of these conditions have a similar immunologic pathogeneses. Canadian and international studies have not only confirmed the presence of these comorbidities but also have demonstrated that patients with psoriasis have a significantly reduced life span. Given that patients with psoriasis are often unaware of their comorbidities, they should be screened for these conditions and treated if required by their dermatologist and/or primary care physician. It is important to keep in mind that the comorbidities and drugs used to treat them have an impact on the choice of antipsoriatic treatment. In addition, comorbidities often preclude the use of traditional systemic agents. Recent studies have demonstrated that patients with preexisting comorbidities can be safely and effectively treated with biologic therapy. Furthermore, literature is evolving to suggest that better control of psoriasis might decrease cardiovascular mortality and prolong life.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".