Limitations of free-form-text diagnostic requisitions as a tool for evaluating adherence to appropriate use criteria for transthoracic echocardiography
Bibliographic record
Abstract
BACKGROUND: Monitoring the adherence to Appropriateness Use Criteria (AUC) has been identified as an important component for the accreditation of echocardiography laboratories. Referral requisitions are a logical tool to rapidly determine the appropriateness of transthoracic echocardiography (TTE) referrals, however data is lacking. We investigated whether standard free-form-text TTE referral requisitions can be used to evaluate AUC adherence. METHODS: Consecutive TTE referral requisitions to the University of Ottawa Heart Institute echocardiography laboratory were reviewed over a four-week period. Indication on the requisition was matched with the relevant indication on the 2011 American College of Cardiology Foundation (ACCF) AUC. Requisitions that did not provide sufficient information to identify the relevant AUC indication were identified as inadequate. For inadequate requisitions, reason for the referral was clarified through medical records and referring physicians. RESULTS: Of the 1303 requisitions, 26.2% did not provide adequate information to determine adherence to AUC, despite a non-adherence (inappropriate) rate of only 6.1% in the referral population. Indication for referral, physician specialty, outpatient status, and prior echocardiogram were independent predictors of inadequate requisitions (p < 0.001, respectively). The most common reasons for inadequate requisitions were a failure to report: 1) change in clinical status, 2) date of a prior echocardiogram, and 3) type and/or severity of a valve lesion. Inclusion of this information would have decreased the inadequacy rate by 56%. CONCLUSION: In a large, academic echocardiography laboratory, over one quarter of free-form-text TTE requisitions are inadequate to evaluate AUC adherence. Structured requisition formats requiring AUC-relevant information are needed to facilitate the practical application of AUC in the echocardiography laboratory.
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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 | 0.131 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".