Improving Clinical Performance Using Rehearsal or Warm-up
Bibliographic record
Abstract
PURPOSE: To determine whether rehearsal (the deliberate practice of skills specific to a procedure) or warm-up (the act or process of warming up by light exercise or practice) prior to performing complex clinical procedures on patients can improve the task performance of operators and operating teams. METHOD: The authors performed an advanced literature search for clinical studies published between 1975 and October 2012 using MEDLINE, EMBASE, the Cochrane Controlled Trials Register, ISI Web of Knowledge, and clinicaltrials.gov. They identified randomized controlled trials and observational studies that evaluated the effects of physical rehearsal or warm-up prior to performing complex clinical procedures. Two reviewers independently reviewed titles and abstracts and then full texts before abstracting data using a standardized form. They resolved disagreements by consensus. RESULTS: The authors identified 1,886 potential articles and included 7 in their review (2 randomized controlled trials and 5 observational studies). All reported that rehearsal or warm-up by operators or operating teams is feasible. Only two clinical studies objectively demonstrated that warm-up can improve overall technical performance. Other objective evidence supporting the positive effects of rehearsal or warm-up for other team or nontechnical outcomes was limited. CONCLUSIONS: The potential benefits of and optimal techniques for performing physical rehearsal and warm-up have not been established. Preliminary findings suggest that preoperative rehearsal or warm-up can improve the performance of operators or operating teams, but there is a paucity of objective evidence and comparative clinical studies in the existing literature to support their routine use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".