Does Warfarin Use Impact Hospital Length of Stay? A Retrospective Study Looking at Patients Treated for Atrial Fibrillation
Bibliographic record
Abstract
The authors received anecdotal practice information from clinicians indicating that when warfarin was initiated in the hospital setting, it may be associated with an increased length of stay (LOS): specifically to achieve a desired minimum international normalized ratio (INR) of 2.0 before discharge in a subset of patients where clinicians perceived follow-up after discharge was not deemed optimal. Given that oral thromboprophylactic anticoagulation with warfarin is the mainstay treatment for the prevention of stroke in atrial fibrillation (AF), the authors decided to look at hospitalized patients from this population to determine if a subset of these patients experienced an increased LOS. The study design entailed a retrospective chart review of consecutive patients admitted to a large, tertiary care, academic center. Patients were included if they were admitted with a primary, secondary, or most responsible diagnosis of paroxysmal or chronic AF. Medical records were audited over an 18-month period (February 1, 2009, to July 31, 2010) to determine the average LOS and to identify patients with a documented prolonged LOS secondary due to subtherapeutic INR at the time of potential discharge. Our final study cohort of 189 patients had an average LOS of 5.2 days (SD = 5.2). However, for eight (4.2%) of these patients discharge was delayed an additional 2.25 days (SD = 1.3) for reasons solely attributed to achieving a therapeutic INR.
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.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".