44 Trainee Perception of a Point of Care Clinical Evidence Module for Bronchiolitis
Bibliographic record
Abstract
Bronchiolitis, a viral induced acute inflammation of the lower respiratory tract is a common reason for hospital admission in infants. A bronchiolitis evidence based clinical module (EBM) was created and applied to the physician order entry (POE) system at an academic paediatric centre. To evaluate trainee perception of a point of care evidence module used to improve physician management of children admitted with bronchiolitis. Questionnaire completed by clinical trainees. Academic pediatric centre. A clinical evidence summary, contained in the Clinical Evidence™ product of BMJ Publishing Group, based on bronchiolitis management was integrated into the Medical Logic Module of the hospital POE system (Sunrise Clinical Manager™; Eclipsys). A 12 question survey examining trainee's use and satisfaction of the module was distributed at the conclusion of the clinical rotation during the peak bronchiolitis admission period (November 01, 2000 and March 31, 2001). We achieved a 90% response rate (50 of 52 surveys). Clinical level of trainees were: third year medical students – 62%; non-pediatric residents – 12%; and pediatric residents – 26%. At least one patient with bronchiolitis was admitted by 94% of the trainee's within the previous month. Despite its easy accessibility on the Bronchiolitis order entry set of the POE system, only 52% of trainees stated that they were aware of the EBC module. Medical students were less likely, while pediatric residents were more likely to have reviewed the EBM (absolute difference 0.58; 95% CI 0.26, 0.76 p<0.001). However, the medical student group were more likely to report that the review was clinically helpful. All groups agreed that point of care evidence was a useful practise and would have additional merit if expanded to other clinical conditions. Point of care evidence has immense capability in clinical care settings to enhance quality and safety of care. More experienced trainees were more likely to utilise readily accessible clinical evidence at the point of care but were less likely to report enhanced educational value compared with less experienced trainees. Methods to increase its adoption and utilisation need to be explored.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".