Pain Among an Inpatient Complex Chronic Care Population of Residents with and without Missing Limbs
Bibliographic record
Abstract
Purpose: Limb loss occurs for various reasons (trauma, infection, vascular diseases, tumors, congenital absence). Limb loss is known to result in several types of pain. Little is known about pain in residents with missing limbs admitted to complex chronic care (CCC) facilities. This study examined the presence of pain and its intensity in CCC residents with and without missing limbs. Methods: The Continuing Care Reporting System was accessed for data from residents admitted to Ontario com\plex chronic care facilities assessed with the Resident Assessment Instrument Minimum Data Set, V2.0. Propensity score matching (1:1 ratio) was used to identify a control resident without missing limbs for each case. McNemar’s test was used for dichotomous pain (Y/N) and Wilcoxon Signed Ranks test for ordinal pain (4-level and 7-level pain variables). Binary and multinomial logistic regression were used to quantify the relationship between missing limbs and reports of pain. Results: Missing limbs were reported by 2961 residents (2.1%, original n=139,920) resulting in 2212 propensity matched pairs. A significantly higher proportion of missing limb cases had pain (80%) versus controls (70%), χ 2 =64.43, p < 0.001. Significantly higher pain levels were found in cases versus controls ( z =8.47, p < 0.001 for 4-level pain; z =8.57, p < 0.001 for 7-level pain). Residents with missing limbs were 1.46 (95% CI: 1.26– 1.70) times more likely to report pain than controls, p < 0.001. Conclusion: The results point to the need to better manage pain in CCC residents with missing limbs. Keywords: complex, disease, chronic, amputation, health
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".