Bibliographic record
Abstract
OBJECTIVES: Assessing pain in elderly persons, who have diminished capacity to communicate verbally, requires use of observational scales that focus upon nonverbal behavior. Facial expression has been recognized as providing the most specific and sensitive nonverbal cues for pain. This study examined the validity of facial expression components of 6 widely used pain assessment scales developed for elders with dementia. Descriptions of the facial expression of pain vary widely on these scales. METHODS: The detailed, anatomically based, objectively coded, and validated Facial Action Coding System was used as a criterion index to provide a definitive description of the facial expression of pain. Thirty elderly inpatients with clinically significant pain in the back or hip, the majority of whom had cognitive impairments, provided videotaped reactions to physical activities. Participants' facial expressions were videotaped during 4 randomly ordered physical activities and coded by a qualified Facial Action Coding System coder. Three 6-second clips indicative of mild, moderate, and severe pain intensities were selected for study for each participant. The 90 clips were coded by 5 raters using the facial expression components of the following observational scales: Doloplus-2, Mahoney, Abbey, pain assessment checklist for seniors with limited ability to communicate, noncommunicative patient's Pain Assessment Instrument, and Pain Assessment in Advanced Dementia. RESULTS: Overall, scales that provided specific descriptions using the empirically displayed facial actions associated with pain yielded greater sensitivity, interjudge reliability, and validity as indices of pain. DISCUSSION: Facial expression items on observational scales for assessing pain in the elderly benefit from adherence to empirically derived descriptions. Those using the scales should receive specific direction concerning cues to be assessed. Observational scales that provide descriptors that correspond to how people actually display facial expressions of pain perform better at differentiating intensities of pain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".