Providing free smoke alarms did not reduce fire related injuries in a deprived multiethnic urban population
Bibliographic record
Abstract
DiGuiseppi C, Roberts I, Wade A, et al. Incidence of fires and related injuries after giving out free smoke alarms: cluster randomised controlled trial. BMJ2002 ; 325 : 995 –7 [OpenUrl][1][Abstract/FREE Full Text][2] QUESTION: Does providing free smoke alarms to a deprived, multiethnic population reduce fires and related injuries? Cluster randomised {allocation concealed},* blinded {clinicians, data collectors, outcome assessors, and data analysts},* controlled trial with 24 months of follow up. 2 boroughs in inner London, UK. 147 444 households in 40 electoral wards with Jarman scores ≥1 standard deviation above the national mean. (The Jarman score is a measure of material deprivation and increased healthcare needs.) Wards were pair matched by Jarman score. 20 wards (73 399 households) were allocated to the intervention, which comprised distribution (door to door and through key local sites) of smoke alarms, with batteries, fittings, and fire safety brochures (in English and other local languages) targeted … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft.stitle%253DBMJ%26rft.aulast%253DDiGuiseppi%26rft.auinit1%253DC.%26rft.volume%253D325%26rft.issue%253D7371%26rft.spage%253D995%26rft.epage%253D995%26rft.atitle%253DIncidence%2Bof%2Bfires%2Band%2Brelated%2Binjuries%2Bafter%2Bgiving%2Bout%2Bfree%2Bsmoke%2Balarms%253A%2Bcluster%2Brandomised%2Bcontrolled%2Btrial%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.325.7371.995%26rft_id%253Dinfo%253Apmid%252F12411355%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=bmj&resid=325/7371/995&atom=%2Febnurs%2F6%2F4%2F105.atom
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".