Additional file 1 of A meta-analysis of the association between physical demands of domestic labor and back pain among women
Bibliographic record
Abstract
Additional files 1: Table 1. Multi-page table detailing the characteristics of Included Studies. Table 2. Risk of bias and overall quality rating using the new castle ottawa scale. Table 3. Quantifying heterogeneity between study sub-groups using Q-statistic. Table 4. List of excluded studies and reasons for exclusion. Figure 1. Baujat plot showing significant contribution to heterogeneity by Alzaharani 2019. Figure 2. Forest Plot with each study’s contribution to heterogeneity, omitted one at a time. Figure 3. Forest plot showing sensitivity analysis of high-quality studies, investigating the association between physically demanding domestic labor and back pain in women. Figure 4. Funnel plot to Assess Publication Bias. Appendix. Search terms culled from two databases.
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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.804 | 0.038 |
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".