Early Removal of Central Venous Catheters and Outcomes from Candidemia
Bibliographic record
Abstract
To the Editor—We read with great interest the observational study by Nucci et al [1], which finds that early removal of central venous catheters was associated with better treatment success and survival in univariable analyses but that this benefit disappeared after adjustment. We wish to share an alternative interpretation and a word of caution. Logistic regression models are commonly used for 2 purposes. First, they may be used to identify predictors for a given outcome: with this approach, variables are selected on the basis of a P value cutoff (eg, <.10 or <.25). This P value answers the question, “Does the model that includes this variable predict the outcome better than a model that does not include this variable?” [2, p 11]. The second purpose is to provide an adjusted estimate for the effect of an explanatory variable after correcting for confounding. For this approach, variables are chosen because they may confound the effect being estimated and because they materially change the odds ratio under study. The P value for the confounder has no role in the decision of whether to adjust for that confounder (there is no statistical test for confounding) [3].
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".