The effect of minimum volume standards in hospitals (MIVOS) — protocol of a systematic review
Bibliographic record
Abstract
Abstract Background The volume-outcome relationship, i.e., higher hospital volume results in better health outcomes, has been established for different surgical procedures as well as for certain nonsurgical medical interventions. Accordingly, many countries such as Germany, the USA, Canada, the UK, and Switzerland have established minimum volume standards. To date, there is a lack of systematically summarized evidence regarding the effects of such regulations. Methods To be included in the review, studies must measure any effects connected to minimum volume standards. Outcomes of interest include the following: (1) patient-related outcomes, (2) process-related outcomes, and (3) health system-related outcomes. We will include (cluster) randomized controlled trials ([C]RCTs), non-randomized controlled trials (nRCTs), controlled before-after studies (CBAs), and interrupted time-series studies (ITSs). We will apply no restrictions regarding language, publication date, and publication status. We will search MEDLINE (via PubMed), Embase (via Embase), CENTRAL (via Cochrane Library), CINHAL (via EBSCO), EconLit (via EBSCO), PDQ evidence for informed health policymaking, health systems evidence, OpenGrey, and also trial registries for relevant studies. We will further search manually for additional studies by cross-checking the reference lists of all included primary studies as well as cross-checking the reference lists of relevant systematic reviews. To evaluate the risk of bias, we will use the ROBINS-I and RoB 2 risk-of-bias tools for the corresponding study designs. For data synthesis and statistical analyses, we will follow the guidance published by the EPOC Cochrane group (Cochrane Effective Practice and Organisation of Care (EPOC), EPOC Resources for review authors, 2019). Discussion This systematic review focuses on minimum volume standards and the outcomes used to measure their effects. It is designed to provide thorough and encompassing evidence-based information on this topic. Thus, it will inform decision-makers and policymakers with respect to the effects of minimum volume standards and inform further studies in regard to research gaps. Systematic review registration PROSPERO CRD42022318883
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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 | 0.120 | 0.128 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.021 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".