A new, female-specific irritability rating scale
Bibliographic record
Abstract
OBJECTIVE: Irritability is a prominent symptom in the spectrum of female-specific mood disorders, and in some women, irritability is serious enough to disrupt their lives and warrant treatment. The objective of this research was to develop a new, female-specific state measure of irritability. METHODS: We constructed self-rating and observer rating scales using items derived from spontaneous descriptions of irritability by women with mood disturbances related to the menstrual cycle, childbearing or menopause. Following a pretest, the scales were shortened to the core items of irritability (annoyance, anger, tension, hostility, sensitivity to noise and touch) and tested on a new cohort of patients. RESULTS: The 14-item Self-Rating Scale and the 5-item Observer Rating Scale showed evidence for internal consistency (Self-Rating: n = 36 patients, Cronbach's alpha = 0.9257, mean interitem correlation = 0.4690; Observer Rating: Cronbach's alpha = 0.7418, mean interitem correlation = 0.3616), Self-Rating test-retest reliability (n = 29 patients, r(s) = 0.704, p = 0.01) and interrater reliability (n = 20 patients; tau(b) = 1.000, p = 0.001). CONCLUSION: This new, female-specific scale for rating irritability has the potential to further the evaluation of this prominent symptom cluster and increase specificity in clinical assessments of emotional disturbances related to reproductive cyclicity in women.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".