A comparison of pain scores and medication use in patients undergoing single-bundle or double-bundle anterior cruciate ligament reconstruction
Bibliographic record
Abstract
BACKGROUND: No gold standard exists for the management of postoperative pain following anterior cruciate ligament reconstruction (ACLR). We compared the pain scores and medication use of patients undergoing single-bundle (SB) or double-bundle (DB) ACLR in the acute postoperative period. Pain and medication use was also analyzed for spinal versus general anesthesia approaches within both surgery types. METHODS: We assessed 2 separate cohorts of primary ACLR patients, SB and DB, for 14 days postoperatively. We used a standard logbook to record self-reported pain scores and medication use. Pain was assessed using a 100 mm visual analogue scale (VAS). Medications were divided into 3 categories: oral opioids, oral nonsteroidal anti-inflammatories and acetaminophen. RESULTS: A total of 88 patients undergoing SB and 41 undergoing DB ACLR were included in the study. We found no significant difference in VAS pain scores between the cohorts. Despite similar VAS pain scores, the DB cohort consumed significantly more opioid and analgesia medication (p = 0.011). Patients who underwent DB with spinal anesthesia experienced significantly less pain over the initial 14-day postoperative period than those who received general anesthesia (p < 0.001). CONCLUSION: Adequate pain relief was provided to all ACLR patients in the initial postoperative period. Patients in the DB cohort experienced more pain, as evidenced by the significant diffrence in consumption of opioids and acetaminophen, than the SB cohort. Patients who underwent spinal anesthesia experienced less pain in the acute postoperative period than those who received general anesthesia.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".