Bibliographic record
Abstract
Background: Chronic postthoracotomy pain (CPP) is defined as an aching or burning sensation 2 months postoperatively along the incision site (lateral or posterior-lateral). Prevalence rates range from 44%–54%. Objective: To define, quantify, and review techniques to prevent and reduce the incidence of CPP. Design and Setting: A comprehensive literature review using OVIDMEDLINE and PUBMED was performed based on preoperative, perioperative, and postoperative techniques that have attempted to reduce the incidence of CPP. Treatment options consisted of preoperative epidural catheters, operative techniques, and postoperative cutaneous extrapleural intercostal nerve block, acupuncture, PCA pumps, gabapentin, or analgesics. Main Outcome Measures: Pain was measured using a visual analog scale (VAS), 4-point scale, McGill Pain Questionnaire, Leeds Assessment of Neuropathic Symptoms and Signs, verbal response scale, numerical rating scale, or a brief pain inventory scale. Results: To date, only gabapentin, transdermal nitroglycerin/etodolac, and acupuncture have produced modest reductions in pain. However, these studies were limited due to either size or length of follow-up. Conclusions: With a lack of definitive treatment options for CPP, and the ability to administer acupuncture to this population of patients, a trial should be conducted to address its efficacy.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".