The 4 item Fracture and Mortality Index predicted hip fracture and all cause mortality in elderly women
Bibliographic record
Abstract
Albertsson DM, Mellstrom D, Petersson C, et al . Validation of a 4-item score predicting hip fracture and mortality risk among elderly women. Ann Fam Med 2007;5:48–56. [OpenUrl][1][Abstract/FREE Full Text][2] Q Can a 4 item risk model predict hip fracture and all cause mortality in elderly women? Clinical impact ratings GP/FP/Primary care ★★★★★★☆ Geriatrics ★★★★★★★ ### ![Graphic][3]</img>Design: prospective cohort study to develop and validate two 4 item risk models based on 5 predefined clinical risk factors for hip fracture. ### ![Graphic][4]</img>Setting: 3 rural primary healthcare districts in Vislanda, Tingsryd, and Emmaboda, Sweden. ### ![Graphic][5]</img>Participants: 1248 women >70 years of age (mean age 79 y) from the National Swedish Population Register. 10% women lived in residential care. ### ![Graphic][6]</img>Description of prediction guide: 2 risk models were tested for prediction of hip fracture and mortality. The Fracture and Mortality (FRAMO) Index (range 0–4) was a summation of 4 predefined clinical risk factors (1 point for each): (1) age ⩾80 years, (2) weight < 60 kg, (3) previous fragility fracture (lower or upper arm, hip, or vertebrae after 40 y of age), and … [1]: {openurl}?query=rft.jtitle%253DThe%2BAnnals%2Bof%2BFamily%2BMedicine%26rft.stitle%253DAnn%2BFam%2BMed%26rft.issn%253D1544-1709%26rft.aulast%253DAlbertsson%26rft.auinit1%253DD.%2BM.%26rft.volume%253D5%26rft.issue%253D1%26rft.spage%253D48%26rft.epage%253D56%26rft.atitle%253DValidation%2Bof%2Ba%2B4-Item%2BScore%2BPredicting%2BHip%2BFracture%2Band%2BMortality%2BRisk%2BAmong%2BElderly%2BWomen%26rft_id%253Dinfo%253Adoi%252F10.1370%252Fafm.602%26rft_id%253Dinfo%253Apmid%252F17261864%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=annalsfm&resid=5/1/48&atom=%2Febmed%2F12%2F4%2F122.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.gif [6]: /embed/inline-graphic-4.gif
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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 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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.007 |
| 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".