Predictors of Postoperative Pain and Analgesic Consumption
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
Pain is a subjective and multidimensional experience that is often inadequately managed in clinical practice. Effective control of postoperative pain is important after anesthesia and surgery. A systematic review was conducted to identify the independent predictive factors for postoperative pain and analgesic consumption. The authors identified 48 eligible studies with 23,037 patients included in the final analysis. Preoperative pain, anxiety, age, and type of surgery were four significant predictors for postoperative pain. Type of surgery, age, and psychological distress were the significant predictors for analgesic consumption. Gender was not found to be a consistent predictor as traditionally believed. Early identification of the predictors in patients at risk of postoperative pain will allow more effective intervention and better management. The coefficient of determination of the predictive models was less than 54%. More vigorous studies with robust statistics and validated designs are needed to investigate this field of interest.
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.
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) | 0.002 | 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