MétaCan
Menu
Back to cohort
Record W2131267898 · doi:10.1186/1745-6215-12-237

The impact of clinical data on the evaluation of tibial fracture healing

2011· article· en· W2131267898 on OpenAlexafffund
Bernadette G Dijkman, Jason W. Busse, Stephen D. Walter, Mohit Bhandari

Bibliographic record

VenueTrials · 2011
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsInstitute for Work & HealthMcMaster University
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsMedicineRadiographyClinical trialBone healingOrthopedic surgeryRandomized controlled trialAdjudicationSurgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Radiographic healing is a common outcome measure in orthopedic trials and adjudication by outcome assessors is often conducted on the basis of plain films alone. The degree to which this process reflects clinical practice, in which both plain films and clinical notes are available, is uncertain. We explored the effect of adding clinical notes to radiographs in the adjudication process of a feasibility trial of tibial shaft fractures. METHODS: Radiographic and clinical data from a multicenter randomized controlled trial of 51 patients with operatively treated tibial fractures formed the basis of the study data. At the completion of the trial, serial radiographs (anteroposterior and lateral) were independently evaluated for progression of fracture healing, defined as bridging of at least 3 of 4 cortices, by an adjudication committee comprised of 3 blinded orthopaedic trauma surgeons. Immediately after determination of radiographic time to healing, each surgeon was provided with clinical notes associated with each radiographic follow up visit and asked to re-visit their initial impression. Consensus was achieved for both adjudications. We calculated the percentage of time to healing consensus decisions that changed after evaluation of clinical notes. We further examined the contents of clinical notes and their relative influence on the committee's decisions. RESULTS: 47 of 51 patients were determined to have healed radiographically during the trial follow-up period, and consideration of clinical notes resulted in a change of 40% (19 of 47) of time to healing consensus decisions; however, revised decisions were equally likely to support an earlier or a later time to healing. Clinical notes that resulted in a change to either a 'healed' or a 'not healed' decision contained significantly more comments of either pain resolution or deterioration, respectively, resumption of or failure to resume weightbearing, or either return or no return to work/pre-injury activities (p < 0.001). CONCLUSIONS: The addition of clinical notes to the adjudication of radiographic fracture healing changed the outcome decision in a substantial number of cases. Orthopedic trialists should consider the addition of clinical notes to adjudication material in studies of fracture healing in order to enhance the generalizability of their results. TRIAL REGISTRATION: The TRUST trial was registered [ID NCT00667849] at http://clinicaltrials.gov/ct2/show/NCT00667849.

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)0.203
metaresearch head score (Gemma)0.395
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.982

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2030.395
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.722
GPT teacher head0.604
Teacher spread0.118 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueTrialsSame topicBone fractures and treatmentsFrench-language works237,207