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Statistical analysis comparison of studies investigating the outcome of hyperbaric oxygen therapy in the management of diabetic foot ulceration

2021· article· en· W4200431605 on OpenAlexaboutno aff
Jianming Zhang, Zhiheng Wang, Xiaoli Ge

Bibliographic record

Venue2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsObservational studyHyperbaric oxygenMedicineRandomized controlled trialDiabetic footIntensive care medicineAdverse effectSample size determinationPhysical therapyDiabetes mellitusInternal medicineSurgery

Abstract

fetched live from OpenAlex

Controversial have been reported from studies evaluating the effect of hyperbaric oxygen therapy (HBOT) on diabetic foot ulceration (DFU), and the recommendations are inconsistent. The study objective was to systematically compare differences in methodological quality and conduct between studies favouring HBOT in the treatment of DFU and those that do not favour it. Secondary analysis of prospective comparative studies of the effectiveness of HBOT in DFU was performed. General information and study design were compared. Studies were classified as favouring HBOT if the primary outcome significantly favoured HBOT and non-favouring HBOT otherwise. Differences in various methodological quality domains were assessed using Cochrane risk of bias tool for randomized controlled trials (RCTs) and Newcastle-Ottawa Scale for observational studies. Thirteen of the 18 included studies favoured HBOT. None of the included studies was multi-centre, and most of them (59%, 10/17) either did not mention funding information or reported no funding. RCTs comprised 78% (14/18); their overall methodological quality was moderate. A few studies favouring HBOT reported an intention-to-treat analysis (30%, 3/10), had sham control (31%, 4/13), and stated the adverse effects of HBOT (46%, 6/13). Of the studies that did not favour HBOT, only one reported sample size calculation; however, it employed an inappropriate outcome. Both those favouring and not favouring HBOT for DFU were of inadequate quality to explain the effect of HBOT on DFU. Sufficiently large and high-quality international, multi-centre, randomized controlled clinical trials are required to formally examine the efficacy of HBOT in DFU.

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)0.091
metaresearch head score (Gemma)0.242
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.483

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.242
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.027
Bibliometrics0.0180.018
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0040.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0180.001

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 head0.030
GPT teacher head0.335
Teacher spread0.305 · 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
DomainMethods
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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venue2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)Same topicDiabetic Foot Ulcer Assessment and ManagementFrench-language works237,207