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A comparison of conventional and minimally invasive multilevel surgery for children with diplegic cerebral palsy

2020· article· en· W3116947767 on OpenAlexaff
Tomos Edwards, Nicky Thompson, R.J. Prescott, Julie Stebbins, James G. Wright, Tim Theologis

Bibliographic record

VenueThe Bone & Joint Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCerebral palsyMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

AIMS: To compare changes in gait kinematics and walking speed 24 months after conventional (C-MLS) and minimally invasive (MI-MLS) multilevel surgery for children with diplegic cerebral palsy (CP). METHODS: A retrospective analysis of 19 children following C-MLS, with mean age at surgery of 12 years five months (seven years ten months to 15 years 11 months), and 36 children following MI-MLS, with mean age at surgery of ten years seven months (seven years one month to 14 years ten months), was performed. The Gait Profile Score (GPS) and walking speed were collected preoperatively and six, 12 and 24 months postoperatively. Type and frequency of procedures as part of MLS, surgical adverse events, and subsequent surgery were recorded. RESULTS: In both groups, GPS improved from the preoperative gait analysis to the six-month assessment with maintenance at 12 and 24 months postoperatively. While reduced at six months in both groups, walking speed returned to preoperative speed by 12 months. The overall pattern of change in GPS and walking speed was similar over time following C-MLS and MI-MLS. There was a median of ten procedures per child as part of both C-MLS (interquartile range (IQR) 8.0 to 11.0) and MI-MLS (IQR 7.8 to 11.0). Surgical adverse events occurred in seven (37%) and 13 (36%) children, with four (21%) and 13 (36%) patients requiring subsequent surgery following C-MLS and MI-MLS, respectively. CONCLUSION: 2021;103-B(1):192-197.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.293
Teacher spread0.228 · 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.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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