Higher Frequency of Psychiatric Morbidity in Patients with Bacterial Infection of the Skin and Subcutaneous Tissue versus Cutaneous Neoplasms: Results from a Nationally Representative Sample from the United States
Bibliographic record
Abstract
BACKGROUND: Poor hygiene and nutrition and resultant compromised immune status in some psychiatric patients can increase susceptibility to bacterial skin infections. OBJECTIVE: We examined the frequency of ICD9-CM psychiatric disorders (codes 290-319) in bacterial skin infections (ICD9-CM codes 680-686) (N = 18,734) versus malignant and benign cutaneous neoplasms (ICD9-CM codes 172, 173, 232, 216) (N = 8,376), conditions that would be expected to cause psychological distress for the patient. METHODS: Logistic regression analysis was conducted controlling for age, sex, race, diabetes, obesity, and the use of antineoplastic and immunosuppressant medications. RESULTS: Skin infections were more commonly (odds ratio = 3.03, 95% CI 1.58-5.82) associated with a psychiatric disorder; the most frequent diagnoses were substance dependence and abuse (19.5%), depressive disorder (19.0%), attention-deficit disorder (14.4%), and anxiety disorders (11.6%). CONCLUSION: In contrast to cutaneous neoplasms, bacterial skin infections were three times as likely to be associated with a psychiatric disorder. Psychiatric comorbidity should be ruled out as a factor in patients with intractable skin infections.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".