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Should Calcaneal Fractures Be Treated Surgically?

2000· review· en· W2030129572 on OpenAlexaff
John A. Randle, Hans J. Kreder, David Stephen, Jack B. Williams, Susan Jaglal, Richard Hu

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2000
Typereview
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsCalgary General HospitalFoothills Medical CentrePublic Health OntarioUniversity of TorontoToronto Public HealthHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCalcaneal fractureCalcaneusSports medicineOrthopedic surgerySurgeryOrthodonticsPhysical therapy

Abstract

fetched live from OpenAlex

A MEDLINE search from 1980 through 1996 revealed 1845 articles dealing with calcaneal fractures. Six of these articles that compared operative versus nonoperative treatment for displaced calcaneal fractures met the minimum criteria for inclusion in a meta-analysis. A statistical summary of information across the six articles revealed a trend for surgically treated patients to be more likely to return to the same type of work as compared with nonoperatively treated individuals. There also was a trend for nonoperatively treated patients to have a higher risk of experiencing severe foot pain than did operatively treated patients. Unfortunately, none of the other outcomes could be summarized formally across studies using statistical techniques because of variability in reporting across studies. Although the tendency was always for operatively treated patients to have better outcomes (reaching statistical significance in some of the articles), the strength of evidence to recommend operative treatment for displaced intraarticular calcaneal fractures remains weak. A large prospective randomized controlled trial should be able to answer this question.

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 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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.304
GPT teacher head0.545
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations186
Published2000
Admission routes1
Has abstractyes

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