The Supportive Care sCore (SCC): trigger alert validation study in solid tumours
Bibliographic record
Abstract
OBJECTIVES: Patients' needs are still underestimated during the course of cancer. The development of a simple and accessible screening tool to screen supportive care needs is an innovative approach to improve the cancer care pathway. The Supportive Care sCore (SCC) is a new tool developed to trigger alerts on the main supportive care needs, such as social, nutritional, physical, pain or psychological disorders. We aimed to develop and validate the SCC tool in identifying supportive care needs. METHODS: The SCC, the Edmonton Symptom Assessment System (ESAS) and the EuroQol-5 Dimension (EQ-5D) questionnaire (for quality of life) were distributed to patients with cancer over a week in an ambulatory hospital of an oncology department. Acceptability was measured by assessing the fill rate. Validity of alerts generated by the SCC was assessed by their consistency with the ESAS and EQ-5D scores. RESULTS: One hundred patients were included, with an average age of 67.2 years. Acceptability was good with a fill rate of over 90%. For a priori-defined risk groups by SCC with alert or not, the ESAS symptom score and quality of life differed significantly (p<0.05) between groups. We observed higher ESAS symptom scores in the alert group (nutritional alert-appetite: 4 (SD 2.4) vs 0 (SD 2.6), p<0.001; physical alert-fatigue: 4 (SD 1.7) vs 2 (SD 2.2), p<0.001; psychological alert-depressed: 3.5 (SD 2.7) vs 0 (SD 1.5), p<0.001). Quality of life was poorer in each domain of the EQ-5D in the alert group. CONCLUSIONS: Our study demonstrates the construct validity of SCC, which holds promise in identifying supportive care needs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".