Development of an Interactive Educational Bedside Assessment Tool with Validation in Headache Clinic (P3.046)
Bibliographic record
Abstract
OBJECTIVE:To validate the bedside assessment tool for headaches in the form of a phone application compared with subspecialty trained neurologists BACKGROUND:There has been a noticeable decrease in bedside assessment skills with an associated reliance on technology for clinically diagnosable conditions in modern physicians that has been lamented for years in several publications. Of these, the prominence of inappropriate neuroimaging for headaches has been noted by the Choosing Wisely and AAN Campaigns. A prototype bedside clinical assessment tool called “SnapDX Clinical” had been developed by the team in partnership with the Internal Medicine Department which is based on the latest rational clinical examination literature intended for use by trainees and clinicians. DESIGN/METHODS:A random sample of 200 adult patient charts from the Headache Clinic in Calgary, Alberta, will be reviewed by two chart reviewers who have undergone training in the data extraction process which will compare the decisions made by clinicians with decisions suggested by the bedside clinical tool. These results will then be analyzed for inter-relater reliability. RESULTS:After development of a preliminary prototype of the application, beta-testing was performed with 68 residents and staff physicians for initial feedback and usability of the app. Overall, 90[percnt] of the group gave encouraging feedback with comments being about the ease of use of the app at the bedside as well as a better appreciation for the relevance of certain signs and symptoms. With the preliminary prototype already developed, the validation of the headache aspect of the application with respect to diagnostic decisions made by headache specialists in the clinic will commence later this year alongside further usability testing. CONCLUSIONS:Preliminary testing shows favourability with using the “SnapDX Clinical” bedside assessment tool however validation in the headache clinic continues to be an ongoing process. Study Supported by:
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".