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Record W1970196265 · doi:10.1080/09638280500056436

Predicting the success of rehabilitation following hip fractures

2005· review· en· W1970196265 on OpenAlexaboutno aff
Leif Ceder

Bibliographic record

VenueDisability and Rehabilitation · 2005
Typereview
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationHip fractureMedicinePhysical therapyQuarter (Canadian coin)Activities of daily livingDiseasePhysical medicine and rehabilitationOsteoporosis

Abstract

fetched live from OpenAlex

PURPOSE: To review what predictors are of positive or negative value in the rehabilitation procedure following hip fractures. METHOD: Reviewing a doctorial thesis on prognosis and rehabilitation of elderly with a hip fracture from 1980 and then review the literature on this subject for the following quarter of a century. RESULTS: In the Western world the short-term prognosis for early return home after sustaining a hip fracture depends on the success of the operation allowing independent walking and basic activities of daily life, no debilitating disease and having someone at home. The one-year prognosis for having returned and remained at home requires a reasonable good health irrespective of living alone and a somewhat deteriorated hip function. CONCLUSIONS: It is difficult to make comparisons with studies of other populations and other time periods. Different predictors of the rehabilitation are used and the definitions of these are not always the same. They are of varying weight, can change with time and may be interdependent of each other. For example, general medical condition and age are strongly interrelated predictors but age alone is less important than concomitant disease for the success of the rehabilitation. Nevertheless, already on admission of a patient with a fresh hip fracture a reliable prognosis can be done. However, such a prediction must be guided by ethical, social and scientific concerns.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.369
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2005
Admission routes1
Has abstractyes

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