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
INTRODUCTION AND OBJECTIVES: Many studies have reported putative factors for the development of chronic pain after surgery. However, advances in knowledge about the etiology and prognosis of chronic postsurgical pain (CPSP) could be gained by improving methodology within studies of surgical pain. The purpose of this study was to review predictive factors and to propose core risk factor and outcome domains for inclusion in future epidemiological studies investigating CPSP. METHODS: Using the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials as a framework we reviewed risk factor and outcome domains, methodological issues and standardized measurement tools based on findings from narrative and systematic reviews, primary clinical and epidemiological studies and published guidelines for chronic pain clinical trials. RESULTS: Five "core" risk factor domains (demographic, pain, clinical, surgery-related, and psychological) and 4 outcome domains (pain, physical functioning, psychological functioning, and global ratings of outcome) were identified. Important methodological issues, related to the definition and timing of follow-up to assess transition from acute to chronic pain are discussed. We also propose the use of validated, standardized measurement tools to capture risk factor and outcome domains at multiple time points. DISCUSSION: There is potential to advance the field of CPSP research by striving for consensus among pain experts; this would advance current evidence by improving our ability to compare findings from different studies and would facilitate the aggregation of surgical cohort datasets to allow international comparisons. We propose these findings as a starting point to build a comprehensive framework for epidemiological studies investigating chronic pain after surgery.
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 | 0.053 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| 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 | 0.001 | 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 it