Bibliographic record
Abstract
OBJECTIVES: To explore variation in the use of diagnostic testing in ICUs, with emphasis on differences between teaching and nonteaching ICUs. DESIGN: Retrospective review of a prospective clinical ICU database. SETTING: Five teaching and four nonteaching ICUs in Winnipeg, Canada, during 2006-2010. PATIENTS: All adults admitted to the nine ICUs during the study period were eligible. After excluding subgroups restricted to teaching ICUs, inter-ICU transfers, prior ICU admission within 90 days, ICU length of stay less than 12 hours, and missing death dates, 10,262 patients were evaluated. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Our primary outcome variable (TotalTesting) was the cumulative number of nine common laboratory tests, three radiologic tests, and electrocardiograms performed in each ICU. We used multivariable median regression to identify factors associated with TotalTesting, including length of stay, demographics, admission details, type and severity of acute illness, and specific medical interventions. We estimated the predictive power of variables as the decline in pseudo-R2 (a goodness-of-fit measure for median regression) when omitting those variables from the model. Median (interquartile range) TotalTesting was 27 (18-49) in teaching ICUs and 20 (13-36) in nonteaching units. With multivariable adjustment, median TotalTesting was 7.1 higher (95% CI, 6.6-7.7) in teaching ICUs. The most influential variable was length of stay, accounting for almost half of the variation. ICU teaching status was the second most important factor, greater than the degree of physiologic derangement and details of medical management. CONCLUSIONS: After adjustment for confounding variables, patients in teaching ICUs had slightly but significantly more diagnostic tests done than those in nonteaching ICUs. In addition to increasing costs, prior studies have shown that excessive testing can cause harm in various ways and does not improve outcomes. Interventions to reduce testing should be directed to all caregivers with responsibility for ordering diagnostic tests, in both teaching and nonteaching institutions.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".