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 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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".