Pain, Health‐Related Quality of Life and Health Care Utilization after Inpatient Surgery: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Little is known about pain-related outcomes in surgical inpatients after discharge from the hospital. An ongoing risk and outcomes monitoring system would provide valuable feedback to improve the quality of patient care. OBJECTIVES: The purpose of the present pilot study was to describe postoperative pain, medication use, health care utilization and health-related quality of life (HRQOL) immediately and four weeks after surgery; merge clinically captured data with Web-based follow-up data; and examine patients' willingness to complete Web-based health questionnaires. METHODS: One hundred two consecutive surgical inpatients were approached for participation. Perioperative data were abstracted from the acute pain management service clinical database and linked to follow-up data captured four weeks postoperatively. RESULTS: Follow-up questionnaires were completed by 88 participants. Clinical assessment data were successfully linked to Web-based follow-up data. Average pain intensity (3.7) four weeks following discharge fell just short of the acute pain management service active pain score of 3.9. At four weeks, all 88 participants reported significantly impaired HRQOL, 36 were still taking pain medications and 15 had visited an emergency room. Two-thirds of the participants had access to the Internet at home and approximately one-half were willing to complete on-line health questionnaires. DISCUSSION: The study indicates that it is feasible to link clinical and research data, and shows a significant burden of pain and reduced HRQOL in the weeks following discharge. This approach to converting clinically captured data into meaningful information about surgical outcomes is valuable in the development of an ongoing risk and outcomes monitoring system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".