Bibliographic record
Abstract
This commentary addresses many of the points made by Penfold and colleagues in the lead article of this issue of Healthcare Papers, including the relationships between hospital standardized mortality ratios (HSMRs) and adverse event reporting, hospital policy and discharge rates. It also discusses what the HSMR is intended to measure, the various analyses and cumulative sum statistic data that my colleagues and I provide to hospitals, interpretation of the results and the inclusion or exclusion of patients receiving comfort or palliative care. It should be noted that my colleagues and I still have the attitude that if anyone can make improvements in our methodologies, we are happy to adopt these improvements as long as they are statistically sound. We feel strongly that if a hospital has a high HSMR, then further investigation is merited to exclude or identify quality-of-care issues; this approach can result in a useful insight into mortality at the institution, which can be associated with a decrease in mortality.
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 | 0.023 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.060 | 0.072 |
| Insufficient payload (model declined to judge) | 0.007 | 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".