The evaluation of an evidence-based clinical answer format for pediatricians
Bibliographic record
Abstract
BACKGROUND: Clinicians are increasingly using electronic sources of evidence to support clinical decision-making; however, there are multiple demands on clinician time, and summarised and synthesised evidence is needed. Clinical Answers (CA) have been developed to address this need; the CA is a synthesised evidence-based summary that supports point-of-care clinical decision-making. The aim of this paper is to report on a survey used to test and improve the CA format. METHODS: An online survey was sent to pediatricians via e-mail and posted on a child health clinical standards website. Quantitative data analysis consisted primarily of descriptive statistics; qualitative data analysis consisted of content analysis. RESULTS: Eighty-three pediatricians responded to the survey. Most respondents found the CA useful or very useful (93%) and agreed or strongly agreed that the layout was effective and allowed them to quickly locate critical information (82%). Quantitative and qualitative data suggested that respondents thought there should be less detail in the linked figures and tables (p = 0.0002), but overall respondents seemed to think there was an appropriate level of detail in most sections of the CA. CONCLUSIONS: Based on the quantitative and qualitative survey responses, major and minor modifications to the CA format were implemented, such as removing forest plots, adding links in each addendum to bring the user back to the front page, and adding an 'Implications for practice' section to the CA. Findings suggest that CAs will be a useful tool for pediatricians; thus, the research team has now begun creating CAs to assist busy clinicians in their day-to-day clinical practice by providing high-quality information for decision-making at the point-of-care.
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 | 0.041 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".