Do Physicians Underestimate Pain in Terminal Cancer Patients? A Prospective Study in a Hospice Setting
Bibliographic record
Abstract
OBJECTIVES: Unrelieved pain is present in a majority of terminal cancer patients. However, the treatment of pain in palliative and hospice care is affected by the lack of validated pain assessment. The goal of this study was to evaluate differences in pain evaluation between terminal cancer patients and physicians and evaluate the pain levels as a survival biomarker. MATERIALS AND METHODS: Patients were evaluated every 7 days for a total of 4 assessments. Physicians evaluated patients' pain on an numeric rating scale (NRS) scale after clinical examination, after which the patients completed NRS, Quality of Life Questionnaire Core 15 Pal (QLQ-C15-PAL), and Edmonton Symptom Assessment System (ESAS) questionnaires. RESULTS: On average, physicians minimally underestimated the pain level in patients (3.47 vs. 3.94 on an NRS scale). Pain was overestimated in 28% and underestimated in 46% of the patients. However, half of all underestimation was clinically meaningful, compared with 28% of the overestimation. For patients with an NRS score of ≥7, pain underestimation was both clinically and statistically significant (5.56 vs. 8.17). Pain ratings exhibited a very small correlation to survival (up to r=-0.22), limiting their use as a survival biomarker. DISCUSSION: Although physicians can accurately assess mild pain in terminal cancer patients in the hospice setting, the underestimation of pain is still clinically significant in almost a quarter of patients, and especially pronounced in patients with higher levels of pain and in female patients. Hence, validated pain assessment is a necessity in hospice care, with the choice of pain evaluation tool dependent on patient and physician preference.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".