Peer Review #2 of "The association between serum lipids and risk of premature mortality in Latin America: a systematic review of population-based prospective cohort studies (v0.1)"
Bibliographic record
Abstract
Objective: To synthetize the scientific evidence on the association between serum lipids and premature mortality in Latin America (LA).Methods: Five data bases were searched from inception without language restrictions: Embase, Medline, Global Health, Scopus and LILACS.Population-based studies following random sampling methods were identified.The exposure variable was lipid biomarkers (e.g., total, LDL-or HDL-cholesterol).The outcome was all-cause and cause-specific mortality.The risk of bias was assessed following the Newcastle-Ottawa criteria.Results were summarized qualitatively.Results: The initial search resulted in 264 abstracts, five (N=27,903) were included for the synthesis.Three papers reported on the same study from Puerto Rico (baseline in 1965), one was from Brazil (1996) and one from Peru (2007).All reports analysed different exposure variables and used different risk estimates (relative risks, hazard ratios or odds ratios).None of the reviewed reports showed strong association between individual lipid biomarkers and allcause or cardiovascular mortality.Conclusion: The available evidence is outdated, inconsistently reported on several lipid biomarker definitions and used different methods to study the long-term mortality risk.These findings strongly support the need to better ascertain the mortality risk associated with lipid biomarkers in LA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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".