Testing The Harvesting Hypothesis By Time-Domain Regression Analysis. Ii: Covariate Effects
Bibliographic record
Abstract
This article extends the previous work of Fung et al. (2004) investigating the ability of the time-scale log-linear regression model, proposed by Dominici et al. (2003) Dominici, F., McDermott, A. and Zeger, S. L. 2003. Airborne particulate matter and mortality: Timescale effects in four US cities. Am. J. Epidemiol., 157: 1055–1065. [PUBMED][INFOTRIEVE][CSA][Crossref], [PubMed], [Web of Science ®] , [Google Scholar], to detect mortality displacement (sometimes known as harvesting) in time-series data relating air pollution to excess mortality. We conducted a simulation study based on two different compartment models of the death process: pure frailty model and mixed frailty model. We assume that nonaccidental death only affects frail population in a pure frailty model and affects both frail people and other individuals in the mixed frailty model. With a pure frailty model and a moderate-size pollution effect, we identified a characteristic mortality displacement pattern in the different time-scale coefficients of log relative risk. However, once a covariate like temperature was introduced into the model, such a mortality displacement pattern disappeared. Furthermore, a false mortality displacement effect was present in the incorrectly specified model, when temperature was not taken into account. We believe that time-scale regression has limited value for detecting mortality displacement in time-series data.
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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 | 0.054 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".