Toward Safer Transitions: How Can We Reduce Post-Discharge Adverse Events?
Bibliographic record
Abstract
Ranjit Kaur is an 83-year-old woman who is brought to the hospital by her son because of worsening shortness of breath over the previous week. The emergency room physician correctly diagnoses a heart failure exacerbation (Wang et al. 2005), initiates appropriate treatment (Felker et al. 2011) and consults the hospitalist physician for admission and ongoing care (Wachter 2004).The hospitalist learns that the patient has been prescribed the various medications recommended by clinical practice guidelines and that her adherence to this medication regimen is excellent. No specific trigger for the heart failure exacerbation is found, and the hospitalist concludes that the most likely explanation is a gradual decline in cardiovascular function, perhaps combined with excessive sodium intake. The day after admission, a dietitian meets with the patient and her daughter-in-law to discuss how her diet could be modified to reduce her sodium intake. Three days after admission, Ms. Kaur is “back to baseline” and ready for discharge. The hospitalist discharges her on a slightly higher dose of her diuretic and instructs Ms. Kaur to see her family physician within a week of discharge. She is sent home with a discharge summary in hand that clearly explains the care provided in hospital and the follow-up plan. In other words, the emergency department and in-patient care are “textbook.” The admission is brief and efficient, there are no complications and Ms. Kaur’s symptoms are substantially improved. Nevertheless, three weeks after discharge, Ms. Kaur is brought back to the emergency department because of confusion. Her blood work in the emergency department shows a dangerously low sodium level. This adverse event may occur after a change in diuretic dose, and can be prevented or managed with careful follow-up after discharge. This all-too-common patient vignette raises three important questions. Why are patients especially vulnerable to adverse events during transitions in care? Are these adverse events preventable? And, if so, how can we prevent them?
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".