The Impact of Standardized Acuity Assessment and a Fast-Track on Length of Stay in Obstetric Triage
Bibliographic record
Abstract
To prospectively assess the impact of a standardized 5-category Obstetrical Triage Acuity Scale (OTAS) and a fast-track for lower-acuity patients on patient flow. Length of stay (LOS) data of women presenting to obstetric triage were abstracted from the electronic medical record prior to (July 1, 2011, to March 30, 2012) and following OTAS implementation (April 1 to December 31, 2012). Following computerized simulation modeling, a fast-track for lower acuity women was implemented (January 1, 2013, to February 28, 2014). Prior to OTAS implementation (8085 visits), the median LOS was 105 (interquartile range [IQR] = 52-178) minutes. Following OTAS implementation (8131 visits), the median LOS decreased to 101 (IQR = 49-175) minutes (P = .04). The LOS did not correlate well with acuity. Simulation modeling predicted that a fast-track for OTAS 4 and 5 patients would reduce the LOS. The LOS for lower-acuity patients in the fast-track decreased to 73 (IQR = 40-140) minutes (P = .005). In addition, the overall LOS (12 576 visits) decreased to 98 (IQR = 47-172) minutes (6.9% reduction; P < .001). Standardized assessment of acuity and a fast-track for lower acuity pregnant women decreased the overall LOS and the LOS of lower-acuity patients.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".