Transthoracic Echocardiography: Impact on Diagnosis and Management in Tertiary Care Intensive Care Units
Bibliographic record
Abstract
The purpose of this study was to evaluate the utility of transthoracic echocardiography (TTE) in an intensive care unit by determining its impact on diagnosis and management. Over a six-month time period, we performed a prospective observational study on all patients admitted to either the medical or the surgical intensive care unit. Structured interviews were conducted with referring physicians before and after the TTE to determine the referring physicians' pre-TTE diagnosis, reasons for requesting the TTE, and whether the TTE resulted in a change in diagnosis and/or management. A total of 135 TTE examinations were done in 126 patients. The referring physicians deemed that clinical information was inadequate to make a definitive diagnosis and management plan in 36/135 (27%) of the requests. In 99/135 (73%) studies, physicians indicated that there was probably sufficient clinical information to formulate a diagnosis and management plan, but ordered a TTE to corroborate their clinical findings. Overall, a change in diagnosis occurred in 39/135 (29%) of studies, and a change in management in 55/135 (41%) of studies. Diagnosis was changed in 19/99 (19%) studies with adequate clinical data, and in 20/36 (56%) studies with inadequate clinical data (P<0.001). Management was changed in 34/99 (34%) of studies with adequate clinical data and in 21/36 (58%) of studies with inadequate clinical data (P=0.017). Of the 62 management changes, 57/62 (92%) changes were minor, and 5/62 (8%) were major. In conclusion we have found that TTE frequently resulted in a change in the diagnosis and management.
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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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".