The Ability of Austrian Qualified Physiotherapists to Make Accurate Keep-Refer Decisions and to Detect Serious Pathologies Based on Clinical Vignettes: Protocol for a Cross-sectional Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: The recognition of serious pathologies affecting the musculoskeletal (MSK) system, especially in the early stage of a disease, is an important but challenging task. The prevalence of such serious pathologies is currently low. However, in our progressing aging population, it is anticipated that serious pathologies affecting the MSK system will be on the rise. Physiotherapists, as part of a wider health care team, can play a valuable role in the recognition of serious pathologies. It is at present unknown how accurately Austrian qualified physiotherapists can detect the presence of serious pathologies affecting the MSK system and therefore determine whether physiotherapy management is indicated (keep patients) or not (refer patients to a medical doctor). OBJECTIVE: We will explore the current ability of Austrian qualified physiotherapists to recognize serious pathologies by using validated clinical vignettes. METHODS: As part of an electronic web-based survey, these vignettes will be distributed among a convenience sample of qualified Austrian physiotherapists working in a hospital or private setting. The survey will consist of four sections: (1) demographics and general information, (2) the clinical vignettes, (3) questions concerning the clinical vignettes, and (4) self-perceived knowledge gaps and learning preferences from the perspective of study participants. Results will further be used for (1) international comparison with similar studies from the existing literature and (2) gaining insight into the participants' self-perceived knowledge gaps and learning preferences for increasing their knowledge level about keep-refer decision-making and detecting serious pathologies. RESULTS: Data collection took place between May 2022 and June 2022. As of June 2022, a total of 479 Austrian physiotherapists completed the survey. Data analysis has started, and we aim to publish the results in 2023. CONCLUSIONS: The results of this survey will provide insights into the ability of Austrian physiotherapists to make accurate keep-refer decisions and to recognize the presence of serious pathologies using clinical vignettes. The results of this survey are expected to serve as a basis for future training in this area. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43028.
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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 | 0.051 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".