Re: "Neighborhood Poverty and Injection Cessation in a Sample of Injection Drug Users"
Bibliographic record
Abstract
I read with great interest the recent Journal article by Nandi et al. (1) on the role of neighborhood poverty as a potential determinant of injection cessation among injection drug users. The authors fitted and contrasted 4 logistic regression models: “crude,” “baseline adjusted,” “fully adjusted,” and one based on inverse-probability weighting (IPW). The results obtained from the IPW model were taken to be valid, whereas those from the other 3 models—including, notably, the “fully adjusted” one—were taken to be biased. Specifically, in the Discussion section of their article, the authors state the following: “These divergent results suggest that use of traditional regression to handle confounding in neighborhood effects studies may induce bias because the individual-level characteristics frequently adjusted for may be time-dependent covariates affected by prior exposure” (1, p. 395). This statement would indeed be true in the context of analysis in which the examination of causal associations with prior exposure(s) measured before the relevant time-dependent covariates were of interest. However, according to the logistic regression models that the authors fitted, this was not the case; only the association of cessation of drug use with recent exposure (i.e., the “level of neighborhood poverty reported at prior visit,” as explicitly stated in the title of Table 2 (1, p. 394)) was estimated and reported, not the association with level of neighborhood poverty at visits before the prior visit. Thus, provided that the set of covariates for adjustment (in the “fully adjusted, traditional” model) and weighting (in the IPW model) is identical, the parameter estimates for the association between recent level of neighborhood poverty and cessation of drug use obtained from the “fully adjusted, traditional” logistic regression model should be no more or less biased than those obtained from the model in which confounding by time-dependent covariates was dealt with by IPW.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".