Using stroke thrombolysis to describe the role of repetition in learning a cognitive skill
Bibliographic record
Abstract
OBJECTIVE: To empirically describe how independent physicians develop a new cognitive clinical skill through repetition using the initiation of a stroke thrombolysis programme as a model. METHODS: This was a retrospective cohort study from April 2009 to March 2013. The setting was a single-centre, Canadian tertiary-care community hospital. The participants were 52 physicians with no prior formal training in stroke thrombolysis assuming a new role of being front-line hyperacute stroke physicians. The main outcome measures were: time needed to accrue experience, door-to-needle time (DTN), with achievement of expertise defined as an average of ≤ 60 minutes, computed tomography (CT)-to-needle time (CTN), with achievement of expertise defined as an average of ≤ 35 minutes, usage of an outside expert stroke telemedicine service, and complication rates with intracranial haemorrhage (ICH). RESULTS: Seven hundred and fifteen cases of hyperacute stroke were seen over the 4-year study period. On average, a physician saw 0.025 cases per hour of code stroke coverage provided; only seven (13.5%) accrued more than 20 code stroke cases and only six (11.6%) ordered thrombolysis more than 10 times. By regression analysis, the average first DTN was 81.0 minutes (95% confidence interval [CI], 77.1-84.9 minutes) and incrementally improved linearly by 0.259 minutes per case seen (95% CI, 0.182-0.337 minutes per case). An estimated 71 cases needed to be seen for the average physician to achieve expertise. Results using CTN were highly similar. Overall, physicians used the external stroke telemedicine providers 23.2% of the time for their first five cases, a rate that decreased to about 5% by the 45th case. Over time, ICH rates were kept at expected benchmarks. CONCLUSIONS: Accruing sufficient experience of a new cognitive clinical skill can be challenging for independent physicians, with expertise gradually emerging in a largely linear fashion only after much repetition.
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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 | 0.000 | 0.004 |
| 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 teacher head, 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".