The Impact of Gender on Interferon-Associated Depression and Anxiety
Bibliographic record
Abstract
Population studies indicate women have higher prevalences of depression and anxiety than men. Interferon (IFN) is a biologic agent that can induce or exacerbate depression and/or anxiety. Whether women are more likely to experience these side effects of IFN during treatment remains to be determined. The aim of this study was to document levels of depression and anxiety in female and male patients before and during IFN-based treatment. This was a prospective open-label study in which depression was measured by Beck Depression Inventory (BDI) and anxiety by Hospital Anxiety and Depression Scale (HADS). Before treatment, the prevalence of depression was higher in females (3/13 [23%]) than males (1/25 [4%]), but the difference did not reach statistical significance (P = 0.12). Initial BDI scores were also higher in females but not significantly (P = 0.07). During treatment, BDI scores increased to a similar extent in both genders. A similar percentage of nondepressed patients at baseline developed depression (females: 50% versus males: 35%, P = 0.45). Before treatment, anxiety was significantly more common in females (7/13 [54%]) than males (3/25 [12%]) (P = 0.016) and median HADS scores were higher in females (P = 0.03). During treatment, increases in HADS scores were similar in the 2 genders. A similar percentage of patients without anxiety at baseline developed anxiety on treatment (females: 50% versus males: 23%, P = 0.31). The frequency and extent of IFN-induced/exacerbated depression and anxiety are not gender dependent.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".