Measuring Total Suffering and Will to Live in an Advanced Cancer Patient Using a Patient-Centered Outcome Measure: A Follow-Up Case Study
Bibliographic record
Abstract
Introduction: The concept of total suffering is well known to palliative care, and it indicates that there are several complex and correlated factors, which contribute to a dynamic and unique experience of one's illness trajectory. Research on terminally ill patients' will to live (WtL) has revealed important insights on its fluctuations over time and its correlated factors. We report an N-of-1 case study with the aim of examining the concept of total suffering objectively, and the WtL trajectory over time, its fluctuations, as well as its possible correlation with other distressing symptoms in a terminally ill cancer patient. Case Description: A 72-year-old cancer patient who verbalized total suffering and a low WtL. We used the Edmonton Symptom Assessment Scale (ESAS), added an additional WtL question, and asked the patient to rate her suffering using the ESAS twice daily (morning and afternoon) for a period of 28 days. Spearman's correlation coefficients between all physical and psychosocial ESAS items were statistical significant in 34 of the 45 performed correlations (30 highly significantly correlations and 4 in a lesser degree). WtL trajectory was fluctuant through the course of the illness, and significant correlations between WtL and all ESAS items were found, except for shortness of breath and drowsiness (after Bonferroni correction). High positive correlations were found between WtL and ESAS total score and ESAS physical and psychological subscores. Discussion: Developing evidence-based understanding of total suffering and WtL in the terminally ill will lead to better approaches to patients and their loved ones.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".