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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".