An algorithm had moderate sensitivity for identifying older women in nursing homes at risk of fracture
Bibliographic record
Abstract
Girman CJ, Chandler JM, Zimmerman SI, et al. Prediction of fracture in nursing home residents. J Am Geriatr Soc2002 ; 50 : 1341 –7 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: Does an algorithm composed of routinely collected baseline data identify older women in nursing homes at increased risk of fracture? 18 month follow up of a cohort of nursing home residents divided into derivation and validation samples. A stratified random sample of 47 long term nursing facilities in Maryland, USA. 1427 white women living in nursing homes who were ≥65 years of age (mean age 85 y), had no terminal cancer or bone metastases, were not comatose, had ≥1 wrist or forearm free of prosthetic implants and open skin lesions, were not admitted for rehabilitation only, and were able to have bone mineral density (BMD) measurements. The women’s most recent minimum data set (MDS) (collected on all nursing home residents in the US) was the primary source of information for the algorithm (total 75 … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Bthe%2BAmerican%2BGeriatrics%2BSociety%26rft.stitle%253DJ%2BAm%2BGeriatr%2BSoc%26rft.aulast%253DGirman%26rft.auinit1%253DC.%2BJ.%26rft.volume%253D50%26rft.issue%253D8%26rft.spage%253D1341%26rft.epage%253D1347%26rft.atitle%253DPrediction%2Bof%2Bfracture%2Bin%2Bnursing%2Bhome%2Bresidents.%26rft_id%253Dinfo%253Adoi%252F10.1046%252Fj.1532-5415.2002.50354.x%26rft_id%253Dinfo%253Apmid%252F12164989%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=10.1046/j.1532-5415.2002.50354.x&link_type=DOI [3]: /lookup/external-ref?access_num=12164989&link_type=MED&atom=%2Febnurs%2F6%2F2%2F58.atom [4]: /lookup/external-ref?access_num=000177256200004&link_type=ISI
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".