OP23 Early Detection Tools For Emotional Distress In Adult Cancer Patients In Spain: A Health Technology Assessment Report
Bibliographic record
Abstract
Introduction Patient-reported outcome measures are being increasingly considered both in clinical practice and in the field of health technology assessment. Although emotional distress is currently recognized as the sixth vital sign in cancer care, its early detection and screening is not yet included in routine clinical practice in Spain. The main objective of this study was to assess the psychometric properties and diagnostic accuracy of validated tools for the early detection of distress among adults with cancer in the Spanish context, at the request of the Spanish National Health System (NHS) Cancer Strategy. Methods A systematic review was carried out to analyze development and validation studies. The Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) was used for the risk of bias assessment, and a multicriteria global assessment was used for the tests. Ethical and organizational aspects were also addressed. Results Fifteen validation studies were included, corresponding to seven tests. The tools considered were the Distress Thermometer (DT), the Brief Symptom Inventory-18 (BSI-18), the Edmonton Symptom Assessment System-revised (ESAS-r), the Hospital Anxiety and Depression Scale (HADS), the Visual Analog Scale for Anxiety and Depression (VAS-AD), the Detection of Emotional Distress (DED) scale, and the Psychosocial and Spiritual Needs Evaluation (ENP-E) scale. Evidence of validity, reliability (internal consistency), and diagnostic accuracy (sensitivity, specificity, and area under the receiver operating characteristic curve) were summarized. Three scales were rated as poor (VAS-AD, BSI-18, and ESAS-r), the ENP-E scale was rated as acceptable, and three scales were rated as moderate (DT, DED, and HADS). Conclusions The DT (single-item measure) stands out as an appropriate tool for early detection of emotional distress in the Spanish NHS. The use of this scale could be considered a first stage, to be combined later with a longer scale to improve screening specificity. The HADS scale could be utilized for this purpose. The use of these tools should be framed within a structured screening program that ensures further evaluation and subsequent psychological care when needed.
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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 | 0.060 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".