Reducing the pain of intramuscular benzathine penicillin injections in the rheumatic fever population of <scp>C</scp>ounties <scp>M</scp>anukau <scp>D</scp>istrict <scp>H</scp>ealth <scp>B</scp>oard
Bibliographic record
Abstract
AIM: To evaluate the effectiveness of lignocaine and a vibrating device with cold pack (Buzzy) for pain management of intramuscular (IM) benzathine penicillin injections in the rheumatic fever (RF) population of Counties Manukau District Health Board (CMDHB). METHODS: Four hundred and five RF patients receiving four weekly injections in the CMDHB region were offered 0.25 mL of lignocaine 2% and Buzzy for pain management of their injections. The lignocaine was mixed in with the benzathine penicillin prior to administration. A pre and post survey assessed pain scores during, 2-min and 1-h post administration and the following day. Questions assessing fear were also included. RESULTS: In total 49% of patients responded to the survey. There were 118 surveys paired pre and post intervention. Pain at injection delivery and fear scores were higher for participants ≤13 years of age. Overall pain scores were significantly reduced over all four time points. There was also a significant reduction in fear of the injections. Lignocaine and Buzzy resulted in a greater reduction in pain than lignocaine alone, only when the injection was being administered to those ≤13 years. After five months, a file audit showed that 66% of all RF patients of CMDHB were choosing to use lignocaine and 43% were choosing to use Buzzy. In total, 71% of all RF patients were choosing one or both of these analgesic interventions. CONCLUSION: This study demonstrates a clinically important reduction in the subjective experience of pain when two analgesic interventions were offered with IM delivery of benzathine penicillin. These pain reduction strategies have been popular in the RF population of CMDHB with a 71% uptake and a corresponding reduction in pain and fear.
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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 | 0.001 |
| 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) | 0.002 | 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".