Bibliographic record
Abstract
We appreciate the thoughtful attention that Dr. Hofer and colleagues have given our article describing the advantages and disadvantages of reliability, or “shrinkage” adjustment.1 Their title, “Current Quality Registries Lack the Accurate Data Needed to Perform Adequate Reliability Adjustments” may be accurate for the anesthesia data collections they mention but not for all surgical quality registries.Dr. Hofer and colleagues’ message about the importance of measurement error cannot be understated. Measurement error, whether in administrative data or in registries, undermines both the validity and utility of quality measurement. When reliability adjustment is applied, unmeasured patient and case-mix factors leave “residual” variation that may be falsely attributed to hospitals or physicians rather than inadequate risk adjustment.2 With or without reliability adjustment, measurement error is critical when benchmarking quality across hospitals or physicians because federal mandates are linking payment to outcome-based performance measurement.Physicians and hospital leaders already appreciate that meaningful outcomes comparisons are very costly to produce, particularly when accrued through a clinical registry and analyzed with the necessary statistical expertise. The first question is whether physicians believe that meaningful outcomes comparisons are important enough to pay for them. The American College of Surgeons (ACS) and the Society of Thoracic Surgeons (STS) staked out their positions on this issue decades ago and currently generate the highest-quality outcomes data in surgery while stewarding multiple measures in the National Quality Forum.3–5 Some may wonder why anesthesiologists have not taken a similar leadership position.6 However, it is important to consider that (1) participation in these registries is costly, (2) neither the ACS nor STS registry outcomes are part of current or proposed Centers for Medicare and Medicaid Services payment programs, and (3) the jury is still out on whether participation in ACS or STS registries improves quality.7,8So how can anesthesiologists improve the quality of quality measurement? This is crucial because mandated links between payment and “performance” are moving forward with or (more commonly) without high-quality measurement science. Solutions are many: investing in anesthesia registries, fostering partnerships with surgeons to share the costs of registries, and uniting with surgeons and nurses for a stronger political voice. In brief, anesthesiologists must either “pony up” the financial and leadership costs of performance measurement or risk being left in the dust.The authors declare no competing interests.
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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.060 | 0.043 |
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".