Bibliographic record
Abstract
Penfold and colleagues, in this issue of Healthcare Papers, provide a comprehensive and substantive critique of the hospital standardized mortality ratio (HSMR) as a measure of patient safety, and suggest a useful alternative. However, although measurement is not trivial, new thinking about patient safety presents a much greater challenge than just issues related to measurement. The measurement issue highlights the need for a re-conceptualization of what it takes, from a systems perspective, to achieve safety. This commentary first reviews Penfold et al.'s arguments (agreeing with their conclusions regarding the HSMR). It then presents some key elements of the new thinking about patient safety, particularly the emerging concepts of resilience and resonance, and notes how and why these are beginning to be applied in healthcare. Finally, it considers a number of reasons why a more comprehensive adoption of these new perspectives may be prolonged and notes that, while difficult, the journey is worth taking.
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 | 0.062 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.059 | 0.092 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".