Using a New Measurement to Evaluate Pain Relief Among Cancer Inpatients with Clinically Significant Pain Based on a Nursing Information System: A Three-Year Hospital-Based Study
Bibliographic record
Abstract
OBJECTIVE: Developing a new measurement index is the first step in evaluating pain relief outcomes. Although the percentage difference in pain intensity (%PID) is the most popular indicator, this indicator does not take into account the goal of pain relief. Therefore, the aims of this study were to develop a pain relief index (PRI) for outcome evaluation and to examine the index using demographic characteristics of cancer inpatients with clinically significant pain. DESIGN: Retrospective cohort study. SETTING: A national hospital. SUBJECTS: All cancer inpatients. METHODS: Pain intensity was assessed using a numerical rating scale, a faces pain scale or the Face, Legs, Activity, Cry, Consolability (FLACC) Behavioral Tool. Using a nursing information system, a pain score database containing data from 2011 through 2013 was analyzed. RESULTS: Cancer patients representing 93,812 hospitalizations were considered in this study. We focused on cancer patients for whom the worst pain intensity (WPI) was ≥ 4 points. PRI values of -62.02% to -72.55% were observed in the WPI ≥ 7 and 4 ≤ WPI ≤ 6 groups. Significant (P < 0.05) effects on PRI values were observed among patients who were > 65 years old, those who were admitted to the medicine or gynecology and those who had a hospital stay > 30 days. CONCLUSION: This hospital-based study demonstrated that the PRI is an effective and valid measure for evaluating outcome data using an electronic nursing information system. We will further define the meaningful range of percentage difference in PRI from various perspectives.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".