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Record W51571119

The SANDHOG criteria and its validation for the diagnosis of DCS arising from bounce diving.

2007· article· en· W51571119 on OpenAlexaboutno aff
Ian Grover, Warren Reed, TS Neuman

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsnot available
Fundersnot available
KeywordsReceiver operating characteristicDecompression sicknessMedicineSensitivity (control systems)Scale (ratio)Point (geometry)DecompressionSurgeryMathematicsEngineeringInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: A three-point scale, the SANDHOG (SAN Diego Diving and Hyperbaric Organizations) criteria, was developed to diagnose DCS (decompression sickness), and then it was validated against a known database of diving related injuries. INTRODUCTION: There are currently no universally accepted diagnostic criteria for the diagnosis of DCS. The SANDHOG criteria were developed to address the need for a case definition of DCS. METHODS: A point scale and entrance criteria were developed for the diagnosis of DCS. Once the entrance criterion had been met, points were awarded based upon the diver's symptoms and their time of onset. The point system and time limits (SANDHOG criteria) were determined based upon US Navy and Royal Canadian diving reports. The SANDHOG criteria were then applied on a post hoc basis to the Duke Hyperbaric database of diving injuries. Sensitivity and specificity were then calculated using three points as the cut off. The ROC (receiver operating characteristic) analysis was performed to determine the area under the curve (AUC). RESULTS: The three point SANDHOG criteria had a specificity of 90.3% and a sensitivity of 52.7%. ROC analysis of the original SANDHOG criteria gave an AUC of 0.72. Using different point values for the diagnosis of DCS will subsequently affect the sensitivity and specificity of the SANDHOG criteria. CONCLUSIONS: The specificity of the SANDHOG criteria is good, and demonstrates that the SANDHOG criteria are a useful tool for the diagnosis of DCS.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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

Citations14
Published2007
Admission routes1
Has abstractyes

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