Observational Pain Assessment Instruments for Use With Nonverbal Patients at the End-of-life: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To review studies pertaining to the reliability and validity of observational pain assessment tools for use with nonverbal patients at the end-of-life, a field of research not documented by previous systematic reviews. METHODS: Databases (PubMed, Embase, Epistemonikos, the Cochrane Library, and CINAHL) were systematically searched for studies from study inception to February 21, 2016 (update in May 9, 2018). Two independent reviewers screened study titles, abstracts, and full texts according to inclusion and exclusion criteria. Disagreements were resolved through consensus. Reviewers also extracted the psychometrics properties of studies of observational pain assessment instruments dedicated to a noncommunicative population in palliative care or at the end-of-life. A comprehensive quality assessment was conducted using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) to derive poor, fair, good or excellent ratings for the psychometric tests reported in each study. RESULTS: Four studies linked to 4 different tools met the inclusion criteria. Study populations included dementia, palliative care and severe illness in the context of intensive care. All the studies included in this review obtained poor COSMIN ratings overall. CONCLUSIONS: At this point, it is impossible to recommend any of the tools evaluated given the low number and quality of the studies. Other analyses and studies need to be conducted to develop, adapt, or further validate observational pain instruments for the end-of-life population, regardless of the disease.
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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 | 0.019 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".