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Development of an Interactive Educational Bedside Assessment Tool with Validation in Headache Clinic (P3.046)

2015· article· en· W355776875 on OpenAlexaffabout
Daniel Fok, Aravind Ganesh, Rahul Mehta, Nathalie Jetté, Lara Cooke

Bibliographic record

VenueNeurology · 2015
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineMedical physics

Abstract

fetched live from OpenAlex

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:

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.067
GPT teacher head0.403
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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