Bibliographic record
Abstract
Pacemakers and implantable cardioverter defibrillators save lives—but only when they work. Although millions of people have these devices, it's hard to say accurately how often they fail and how often the patient's life is at risk when they do. All we know is that malfunction is not a rare event. Credit: JAMA After appraising two new papers on the subject (pp 1901-6 and 1929-34), one commentator estimates that about seven in every 1000 pacemakers and about 21 in every 1000 cardioverter defibrillators malfunction badly enough to need replacing. Neither of these estimates included devices that broke down because of faulty leads. Nor did the estimates include people who died as a result of their device malfunctioning. Unreliable reporting means that both estimates are likely to be lower than the real rate, which, for pacemakers at least, seems to be going down. The trend for cardioverter defibrillators looks less predictable. Credit: JAMA Information on the safety of these devices is a complex mosaic, the author writes, and many of the pieces are still missing. What, for example, should doctors do when regulators issue a safety warning about a particular model? In a third paper (pp 1907-11), doctors from Canada electively replaced the “faulty” cardioverter defibrillator in about a fifth of their patients, although some replaced none and others replaced 45% (24/53). In 6% (31/533) of patients, the elective replacement caused serious surgical complications, most commonly bleeding and infections. Two patients died. JAMA2006;295: 1944–6 [OpenUrl][1][CrossRef][2][PubMed][3] Poor US women often miss out on recommended cancer screening services. In an attempt to reach them, researchers trained “prevention coaches” to telephone and motivate women to keep up to date with their cervical screening, mammography, and colorectal cancer screening. The coaches used a partially scripted interview technique to find out why women weren't attending and how any barriers to … [1]: {openurl}?query=rft.jtitle%253DJAMA%26rft.stitle%253DJAMA%26rft.issn%253D0002-9955%26rft.aulast%253DWilkoff%26rft.auinit1%253DB.%2BL.%26rft.volume%253D295%26rft.issue%253D16%26rft.spage%253D1944%26rft.epage%253D1946%26rft.atitle%253DPacemaker%2Band%2BICD%2Bmalfunction--an%2Bincomplete%2Bpicture.%26rft_id%253Dinfo%253Adoi%252F10.1001%252Fjama.295.16.1944%26rft_id%253Dinfo%253Apmid%252F16639055%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/external-ref?access_num=10.1001/jama.295.16.1944&link_type=DOI [3]: /lookup/external-ref?access_num=16639055&link_type=MED&atom=%2Fbmj%2F332%2F7549%2F1084.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
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: Editorial 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 | medium |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".