Effects of an Alternate Payment Plan on Pediatric Surgical Practice in an Academic Setting: The Role of Corporate Indicators
Bibliographic record
Abstract
The objectives of this study were to describe the effects of an alternate payment plan (APP) on clinical surgical practice--at eight surgical divisions of Children's Hospital of Eastern Ontario--and to emphasize the important role of corporate indicators (CIs). To do this, we analyzed CIs comparing two years before the implementation of the APP with years one and two and years three and four post-implementation. The number of in-hospital consultations decreased in division two comparing pre-APP encounters with years one and two and years three and four post-APP. There was no difference between years one and two and three and four post-APP. Encounters in outpatient clinics increased in divisions four and seven. Division six had a decrease in encounters comparing pre-APP with years one and two and years three and four post-APP. There was no difference between years one and two and three and four post-APP. Encounters for same-day surgery increased in division six after the implantation of the APP; division two had a decrease comparing pre-APP with years one and two and years three and four post-APP. No difference was seen between years one and two and three and four post-APP. For in-patient surgery, only division eight had a significant decrease in encounters comparing pre-APP with years one and two and years three and four post-APP. There was no difference between years one and two and three and four post-APP. This study demonstrates that the APP has had little influence on patterns of clinical practice in our institution, which is the sole pediatric referral centre for the region. Since CIs are produced on a yearly basis by the institution and physicians have no influence on the data, CIs may play a role in replacing shadow billing as a way of measuring healthcare service delivery in an APP setting on academic institutions.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".