Trends in adverse events over time: why are we not improving?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With widespread interest and investments in patient safety in the 13 years following the US Institute of Medicine report To Err is Human,1 the question has under-standably arisen: have we decreased medical harm? One widely cited study showed no significant reductions in either the overall rate of harm or the rate of preventable harm in 10 US hospitals chosen on the basis of patient safety activities.2 A second US study,3 though not focused on temporal trends, reported that one third of patients suffered harm from their medical care at three tertiary care hospitals recognised for their efforts in improving patient safety. Given that previous major studies reported adverse event rates in the range of 3–16%,4–10 progress seems sorely lacking. Adding to this distressing picture, Baines et al11 report in this issue of the journal that the adverse event rate among hospitalised patients in the Netherlands increased from 4.1 % in 2004 to 6.2 % in 2008. Somewhat reassuringly, preventable adverse rate did not change. The increase in non-preventable adverse rates may reflect better documentation in medical records as a result of interest in patient safety, with the stable rate of preventable events suggesting that safety has not actu-ally worsened. Nonetheless, the main message of this study11 and the two pre-vious ones2 3 remains: sustained attention to patient safety has failed to produce widespread reductions in rates of harm medical care.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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 it