Text Messaging Reduces Analgesic Requirements During Surgery
Bibliographic record
Abstract
OBJECTIVE: This study aims to determine whether communicating via short message service text message during surgery procedures leads to decreased intake of fentanyl for patients receiving regional anesthesia below the waist compared with a distraction condition and no intervention. METHODS: Ninety-eight patients receiving regional anesthesia for minor surgeries were recruited from a hospital in Montreal, QC, between January and March 2012. Patients were randomly assigned to text message with a companion, text message with a stranger, play a distracting mobile phone game, or receive standard perioperative management. Participants who were asked to text message or play a game did so before receiving the anesthetic and continued until the end of the procedure. RESULTS: The odds of receiving supplemental analgesia during surgery for patients receiving standard perioperative management were 6.77 (P=0.009; N=13/25) times the odds for patients in the text a stranger condition (N=22/25 of patients), 4.39 times the odds for those in the text a companion condition (P=0.03; N=19/23), and 1.96 times the odds for those in the distraction condition (P=0.25; N=17/25). CONCLUSION: Text messaging during surgery provides analgesic-sparing benefits that surpass distraction techniques, suggesting that mobile phones provide new opportunities for social support to improve patient comfort and reduce analgesic requirements during minor surgeries and in other clinical settings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.007 | 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".