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 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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".