Physician Practice Consolidation Driven By Small Acquisitions, So Antitrust Agencies Have Few Tools To Intervene
Bibliographic record
Abstract
The growing concentration of physician markets throughout the United States has been raising antitrust concerns, yet the Department of Justice and the Federal Trade Commission have challenged only a small number of mergers and acquisitions in this field. Using proprietary claims data from states collectively containing more than 12 percent of the US population, we found that 22 percent of physician markets were highly concentrated in 2013, according to federal merger guidelines. Most of the increases in physician practice size and market concentration resulted from numerous small transactions, rather than a few large transactions. Among highly concentrated markets that had increases large enough to raise antitrust concerns, only 28 percent experienced any individual acquisition that would have been presumed to be anticompetitive under federal merger guidelines. Furthermore, most acquisitions were below the dollar thresholds that would have required the parties to report the transaction to antitrust authorities. Under present mechanisms, federal authorities have only limited ability to counteract consolidation in most US physician markets.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".