Clinical Decision Rules "in the Real World": How a Widely Disseminated Rule Is Used in Everyday Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Widespread, appropriate use of clinical decision rules would result in many benefits for health care. While it is known that clinicians report using these rules, little is known about how the rules are actually used in everyday practice. OBJECTIVES: To conduct a survey of emergency physicians to examine whether they use the Ottawa Ankle Rules (OAR) consistently, exclusively, and accurately. METHODS: A postal survey was administered to 399 emergency physicians randomly selected from the membership list of the Canadian Association of Emergency Physicians using Dillman's tailored design method for postal surveys. Results were analyzed via frequency distributions and linear regression. RESULTS: Response rate was 69.7% (262 of 376 eligible respondents), of whom 99.2% were familiar with the OAR. Most physicians (89.6%) reported using the OAR always or most of the time in appropriate circumstances, while only 42.2% reported basing their decisions to order radiography primarily on the rule. Physicians reported considering non-rule factors that are not related to the presence of a fracture (e.g., swelling: 54%), and factors that add no more predictive value over and above the rule (e.g., age >55 years: 55.2%). While 82.4% reported not having reviewed the rule for months or years, only 30.9% of the respondents were able to correctly remember the components of the rule. Errors in remembering rule components were more common among part-time (beta = 0.18, p = 0.009) and older (beta = 0.18, p = 0.04) physicians, and those who do not apply the rule consistently (beta = 0.14, p = 0.04). CONCLUSIONS: Most physicians report using and applying the OAR consistently, but most report that the rule is not the primary determinant of their decisions. Most apply this rule without referring to memory aids, yet their memory for this simple rule is imperfect. Future work should study how different memory aid strategies might improve the accuracy of rule application and reduce the use of nonpredictive cues.
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.
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 | 0.008 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it