Pain descriptors for critically ill patients unable to self‐report
Bibliographic record
Abstract
AIM: To examine descriptors used by nurses in two Canadian intensive care units to document pain presence for critically ill patients unable to self-report. BACKGROUND: Systematic documentation of pain assessment is essential for communication and continuity of pain management, thereby enabling better pain control, maximizing recovery and reducing physical and psychological sequelae. METHOD: A retrospective, mixed method, having observational design in two Level-III intensive care units of a quaternary academic centre in Toronto, Canada. During 2008-2009, data were abstracted via chart review guided by a reference compendium of potential behavioural descriptors compiled from existing behavioural pain assessment tools. RESULTS: A total of 679 narrative descriptions were extracted. Behavioural descriptors (232, 34%), physiological descriptors (93, 14%), and descriptors indicating the patient was pain free (117, 17%) were used to describe pain presence or absence. Narratives also described analgesia administered without descriptors of pain assessment (117, 17%) and assessment and analgesic administration prior to a known painful procedure (30, 4%). Emerging themes included life-threatening treatment interference, decisional uncertainty and a wakefulness continuum. CONCLUSION: Inconsistent or ambiguous documentation was problematic in this sample. This may reflect confounding behaviours and concomitant safety priorities. Developing a lexicon of pain assessment descriptors of critically ill patients unable to self-report for use in combination with valid and reliable measures may improve documentation facilitating appropriate analgesic management. Protocols or unit guidelines that prioritize a trial of analgesia before administration of sedatives may decrease decisional uncertainty when patients exhibit ambiguous behaviours such as agitation or restlessness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.035 |
| 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) | 0.000 | 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 teacher head, 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".