Correlation of COOP/WONCA charts with the Spanish version of the Nottingham Health Profile for oncology patients and the Beck Inventories: Clinical implications
Bibliographic record
Abstract
Background: The rising prevalence of cancer presents a major public health challenge in Spain and globally. In 2023, the Spanish Society of Medical Oncology (SEOM) projected 279,260 new cancer cases in Spain, with colon, breast, lung, prostate, and bladder cancers being predominant. Cancer remains a leading cause of death worldwide, with 18.1 million new cases and 9.5 million deaths in 2018, expected to rise significantly by 2040. Given these alarming statistics, there is an urgent need to address the complex needs of cancer patients. This study assesses the psychometric properties of the COOP/WONCA charts and Nottingham Health Profile (NHP) in oncology patients, evaluating their correlation with the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-2). Methods: An analytical observational cohort study included oncology patients from the General Hospital of Elche, Spain, undergoing chemotherapy and/or radiotherapy. The COOP/WONCA charts, NHP, BAI, and BDI-2 assessed health-related quality of life (HRQoL) and psychological states at baseline, 15 days after treatment initiation, monthly during treatment, and at the end of treatment. Correlations were analyzed using Pearson and Spearman coefficients. Results: Among 75 patients (36% men, 64% women), significant correlations were observed between COOP/WONCA charts and NHP dimensions, including energy (rho = 0.560), pain (rho = 0.520), physical movement (rho = 0.718), emotional reaction (rho = 0.662), sleep (rho = 0.486), social isolation (rho = 0.674), and functionality (rho = 0.778), all p < 0.001. HRQoL improvements were significantly correlated with reductions in anxiety and depression. Conclusion: The COOP/WONCA charts are effective tools for assessing HRQoL in oncology patients, correlating strongly with the NHP and psychological states measured by BAI and BDI-2. Future research should explore their applicability in diverse clinical settings and the development of personalized interventions integrating HRQoL assessments.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".