Establishing the sensitivity and specificity of the gynaecological cancer distress screen
Bibliographic record
Abstract
OBJECTIVE: Nuanced distress screening tools can help cancer care services manage specific cancer groups' concerns more efficiently. This study examines the sensitivity and specificity of a tool specifically for women with gynaecological cancers (called the Gynaecological Cancer Distress Screen or DT-Gyn). METHODS: This paper presents cross-sectional data from individuals recently treated for gynaecological cancer recruited through Australian cancer care services, partner organisations, and support/advocacy services. Receiver operating characteristics analyses were used to evaluate the diagnostic accuracy of the DT-Gyn against criterion measures for anxiety (GAD-7), depression (patient health questionnaire), and distress (IES-R and K10). RESULTS: Overall, 373 individuals aged 19-91 provided complete data for the study. Using the recognised distress thermometer (DT) cut-off of 4, 47% of participants were classified as distressed, while a cut-off of 5 suggested that 40% had clinically relevant distress. The DT-Gyn showed good discriminant ability across all measures (IES-R: area under the curve (AUC) = 0.86, 95% CI = 0.82-0.90; GAD-7: AUC = 0.89, 95% CI = 0.85-0.93; K10: AUC = 0.88, 95% CI = 0.85-0.92; PHQ-9: AUC = 0.85, 95% CI = 0.81-0.89) and the Youden Index suggested an optimum DT cut-point of 5. CONCLUSIONS: This study established the psychometric properties of the DT-Gyn, a tool designed to identify and manage the common sources of distress in women with gynaecological cancers. We suggest a DT cut point ≥5 is optimal in detecting 'clinically relevant' distress, anxiety, and depression in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".