Innovations and opportunities for primary health care after hospital discharge: an application of causal inference methods in health services research
Bibliographic record
Abstract
Based on Clinical Classification Software (CCS) for ICD-9CM and for ICD-10-CM.** ICD-9-CM and ICD-10-CMCodes for AMI, COPD and CHF were checked manually to match Quebec definitions.*** Includes encounters for follow-up after medical or surgical interventions (e.g., aftercare following organ transplant), encounters for palliative care, long-term and current use of specific medications (e.g.long-term use of opiate analgesic).* We interpreted as evidence of positivity or propensity score model misspecification if the mean of the stabilized weight was far from one or if there were extreme values.** Clustered standard errors.*** Calculated for each day after discharge.Corresponds to the difference between groups divided by the pooled standard error.We interpreted a value greater than 10% as a meaningful difference between the groups.Standardized differences are less sensitive to sample size.* Based on Clinical Classification Software (CCS) and Canadian Institute for Health Information; ICD-9-CM and ICD-10-CM codes were checked manually to match Quebec diagnosis codes.Notes: Horizontal lines represent clustered 95% CIs.Vertical line represents null association.* Covariates and two-way interactions adjusted for are listed in Appendix 6.3.A negative number favors team-based primary care models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".