Developing a Computerized Data Collection and Decision Support System for Cancer Pain Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contemporary nursing practice needs reengineering to deliver its service effectively and efficiently. Using computer technology to support clinicians' decision making may be a parsimonious way to provide high-quality, patient-centered, efficient care. The process of developing the PAINReportIt and PAINConsultN system is described, and the results of two pilot studies in which the system was tested are summarized. The feasibility of using the system to assess pain and provide decision support for clinicians is demonstrated. The findings show PAINReportIt to be promising as an effective, efficient way for patients to report their pain. Whether PAINConsultN is an effective answer to cancer pain management barriers warrants further evaluation with larger samples. The advantages of using the system, as compared with use of the traditional pain management process, are discussed.
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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 | 0.001 | 0.000 |
| 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 it