MétaCan
Menu
Back to cohort
Record W2142919819 · doi:10.1136/ebm.10.4.118

Review: early mobilisation is better than cast immobilisation for injured limbs

2005· article· en· W2142919819 on OpenAlexaff
Hans J. Kreder

Bibliographic record

VenueEvidence-Based Medicine · 2005
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineWeb of scienceCochrane LibraryRandomized controlled trialSystematic reviewPhysical therapyMeta-analysisInternal medicineMEDLINE

Abstract

fetched live from OpenAlex

Nash CE, Mickan SM, Del Mar CB, et al. Resting injured limbs delays recovery: a systematic review. J Fam Pract 2004;53:706–12. [OpenUrl][1][PubMed][2][Web of Science][3] Q In patients with acute limb injuries, is early mobilisation better than rest (cast immobilisation)? Clinical impact ratings GP/FP/Primary care ★★★★★☆☆ Emergency medicine ★★★★★☆☆ ### ![Graphic][4]</img>Data sources: Cochrane Controlled Trials Register, Cochrane Database of Systematic Reviews, Medline (1966–2002), EMBASE/Excerpta Medica, Web of Science, and references of retrieved studies. ### ![Graphic][5]</img>Study selection and assessment: randomised controlled trials (RCTs) in any language that compared early mobilisation with cast immobilisation in patients with acute limb injuries, had ⩾80% follow up, and included patients who were not predominantly young children. Quality assessment of the individual studies was based on the criteria of the Cochrane Musculoskeletal Injuries Group (maximum score 18). ### ![Graphic][6]</img>Outcomes: patient centred outcomes (pain and swelling and satisfaction), functional outcomes (range of motion, days lost from work, and return to sport), and complications. 49 RCTs met the selection criteria. 16 trials were considered to be of … [1]: {openurl}?query=rft.jtitle%253DThe%2BJournal%2Bof%2Bfamily%2Bpractice%26rft.stitle%253DJ%2BFam%2BPract%26rft.aulast%253DNash%26rft.auinit1%253DC.%2BE.%26rft.volume%253D53%26rft.issue%253D9%26rft.spage%253D706%26rft.epage%253D712%26rft.atitle%253DResting%2Binjured%2Blimbs%2Bdelays%2Brecovery%253A%2Ba%2Bsystematic%2Breview.%26rft_id%253Dinfo%253Apmid%252F15353159%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=15353159&link_type=MED&atom=%2Febmed%2F10%2F4%2F118.atom [3]: /lookup/external-ref?access_num=000224000300009&link_type=ISI [4]: /embed/inline-graphic-1.gif [5]: /embed/inline-graphic-2.gif [6]: /embed/inline-graphic-3.gif

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.005
Bibliometrics0.0060.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.143
GPT teacher head0.470
Teacher spread0.327 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview · Commentary

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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueEvidence-Based MedicineSame topicNursing Roles and PracticesFrench-language works237,207