Bibliographic record
Abstract
We thank VanderWeele and Kim for their letter in response to our commentary on the scientific challenges—and opportunities—presented by benchmarking social isolation and loneliness with cigarette smoking.1,2 We appreciate the methodological considerations they offer—in particular, the additional limitations raised by using population-attributable fractions to benchmark social isolation and loneliness and cigarette smoking. Owing to a paucity of prospective studies allowing direct comparison of absolute risk differences, we were unable to complement our analysis with a comparison of absolute risk differences, as suggested. We agree it is important to complement comparisons of population-attributable fractions or relative risk with comparisons of absolute risk difference when benchmarking. We thank VanderWeele and Kim for highlighting the advantages of benchmarking within the same study population using consistent methods of analysis and, thus, the opportunities presented by exposure-wide study designs. Finally, regarding the risk estimates abstracted and transformed to generate Figure 6 of the original paper by Holt-Lunstad et al.3 (presented as Figure 1 in our commentary1), we wish to clarify that the “light” smoking category reported by Shavelle et al.4 was used across studies with different sample sizes. This category had an overall median of fewer than 15 cigarettes per day and an average of fewer than 13.3 cigarettes per day.4 No funding to report. J.H.-L. acknowledges an unpaid role as chair of the Scientific Advisory Council for the Foundation for Social Connection and receipt of payment for consulting fees from the Triple-S Foundation. The other authors declare no conflicts.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.036 | 0.038 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".