<i>Lidocaine Patches Reduce Pain in Trauma Patients with Rib Fractures</i>
Bibliographic record
Abstract
Rib fracture pain is notoriously difficult to manage. The lidocaine patch is effective in other pain scenarios with an excellent safety profile. This study assesses the efficacy of lidocaine patches for treating rib fracture pain. A prospectively gathered cohort of patients with rib fracture was retrospectively analyzed for use of lidocaine patches. Patients treated with lidocaine patches were matched to control subjects treated without patches. Subjective pain reports and narcotic use before and after patch placement, or equivalent time points for control subjects, were gathered from the chart. All patients underwent long-term follow-up, including a McGill Pain Questionnaire (MPQ). Twenty-nine patients with lidocaine patches (LP) and 29 matched control subjects (C) were analyzed. During the 24 hours before patch placement, pain scores and narcotic use were similar (LP 5.3, C 4.6, P = 0.19 and LP 51, C 32 mg morphine, P = 0.17). In the 24 hours after patch placement, LP patients had a greater decrease in pain scores (LP 1.2, C 0.0, P = 0.01) with no change in narcotic use (LP -8.4, C 0.5-mg change in morphine, P = 0.25). At 60 days, LP patients had a lower MPQ pain score (LP 7.7, C 12.2, P < 0.01), although only one patient was still using a patch. There was no difference in time to return to baseline activity (LP 73, C 105 days, P = 0.16) and no adverse events. Lidocaine patches are a safe, effective adjunct for rib fracture pain. Lidocaine patches resulted in a sustained reduction in pain, outlasting the duration of therapy.
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 | 0.001 |
| 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) | 0.001 | 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".