Associations between water exposure and the development of amyotrophic lateral sclerosis: a matched case-control study
Bibliographic record
Abstract
OBJECTIVE: Previous studies have hinted at an association between water exposure and the development of ALS. However, proximity measures to these water sources have been limited to questionnaires or large buffers due to a lack of fine geospatial measures. They also do not distinguish the various classes of hydrographic features. Thus, we created a robust database to investigate the association between proximity to water bodies at place of residence and the development of ALS. METHODS: A matched (sex and year of birth) case-control study was conducted in New Brunswick, Canada from January 2003 to February 2021. Study population included 304 ALS patients and 1207 controls with their historical postal codes linked to spatial proximity datasets and air pollution index indicators (proxy measures for contamination by run-off). RESULTS: Odds of ALS were not significantly associated with proximity to water bodies, even within a 250 m buffer from place of residence (Oceans: 1.10, 0.60-2.00 [95% CI], Reservoirs/Ponds/Lakes: 1.24, 0.47-3.30 [95% CI]). As for interaction models investigating proximity to potentially contaminated water bodies, none of the final fitted models observed an association between proximity to water bodies with indicators of potential run-off sources and the development of ALS. CONCLUSIONS: No significant association between proximity to water bodies at place of residence and the development of ALS were observed in the current study. Future studies should consider taking direct measurements of water quality or utilize geomaps of spraying activities and cyanobacteria blooms alongside proximity measures. Household water quality is another avenue to explore, particularly well water use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".