How do healthcare workers judge pain in older palliative care patients with delirium near the end of life?
Bibliographic record
Abstract
OBJECTIVE: Pain and delirium are commonly reported in older people with advanced cancer. However, assessing pain in this population is challenging, and there is currently no validated assessment tool for this task. The present retrospective cohort study was conducted to understand how healthcare workers (HCWs; nurses and physicians) determine that older cancer patients with delirium are in pain. METHOD: We reviewed the medical records of consecutive palliative care inpatients, 65 years of age and above (N = 113), in order to identify patient-based cues used by HCWs to make pain judgments and to examine how the cues differ by delirium subtype and outcome. RESULTS: We found that HCWs routinely make judgments about pain in older patients with delirium using a repertoire of strategies that includes patient self-report and observations of spontaneous and evoked behavior. Using these strategies, HCWs judged pain to be highly prevalent in this inpatient palliative care setting. SIGNIFICANCE OF RESULTS: These novel findings will inform the development of valid and reliable tools to assess pain in older cancer patients with delirium.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".