The Psychosocial Screen for Cancer (PSSCAN): Further validation and normative data
Bibliographic record
Abstract
BACKGROUND: We have previously reported on the development of a cancer-specific screening instrument for anxiety and depression (PSSCAN). No information on cut-off scores or their meaning for diagnosis was available when PSSCAN was first described. Needed were additional analyses to recommend empirically justified cut-off scores as well as data norms for healthy adult samples so as to lend meaning to the recommended cut-off scores. METHODS: We computed sensitivity/specificity indices based on a sample of 101 cancer patients who had provided PSSCAN data on anxiety and depression and who had completed another standardized instrument with strong psychometrics. Next, we compared mean scores for four samples with known differences in health status, a healthy community sample (n = 561), a sample of patients with a representative mix of cancer subtypes (n = 570), a more severely ill sample of in-patients with cancer (n = 78), and a community sample with a chronic illness other than cancer (n = 85). RESULTS: Sensitivity/specificity analyses revealed that an excellent balance of sensitivity/specificity was achievable with 92%/98% respectively for clinical anxiety and 100% and 86% respectively for clinical depression. Newly diagnosed patients with cancer were no more anxious than healthy community controls but showed elevations in depression scores. Both, patients with chronic illness other than cancer and those with longer-standing cancer diagnoses revealed greater levels of distress than newly diagnosed cancer patients or healthy adult controls. CONCLUSION: These additional data on criterion validity and community versus patient norms for PSSCAN serve to enhance its utility for clinical practice.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".