Bibliographic record
Abstract
Objective To screen and obtain relevant evidence on selection and insertion of short peripheral intravenous catheters, and to summarize the best available evidence. Methods We searched the BMJ best practice, Joanna Briggs institute (JBI), Cochrane library, registered nurses, association of Ontario (RNAO), infusion nurses society (INS), national guideline clearinghouse (NGC), national institute for health and care excellence (NICE), Medlive, etc, to collect literature including clinical practice guideline (recent five years), evidence summary and systematic review regarding selection and insertion of short peripheral intravenous catheters. Two reviewers independently performed quality appraisal for included studies, and evidence was extracted for those meeting quality requirements. Results Combined with judgment of clinical professionals, totally 18 evidences selected, including indications for infusion, selection of catheter and puncture site, patient education, preparation of tourniquet and puncture site, application of anesthetics, measures for difficult or failed catheterization, catheter replacement and dressing fixation. Conclusion Nurses should pay attention to indications for intravenous infusion, cautiously select target veins to guarantee patient safety; for patients with difficult catheterization, visualization, warming, and painless techniques should be actively adopted to improve the success rate of catheterization and reduce patients’ discomfort.
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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 | 0.041 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.028 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".