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Record W2464858643 · doi:10.4193/rhino15.072

Stratification of SNOT-22 scores into mild, moderate or severe and relationship with other subjective instruments

2016· article· en· W2464858643 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRhinology Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineInterquartile rangeChronic rhinosinusitisNasal polypsQuality of life (healthcare)Internal medicine

Abstract

fetched live from OpenAlex

AIMS AND OBJECTIVES: The European Position Paper on Rhinosinusitis and Nasal Polyps provides treatment algorithms based on the mild/moderate/severe (MMS) classification. To date there has been no statistically validated stratification of the SNOT-22 score according to this classification. METHODS: 65 consecutive patients diagnosed with CRS completed a SNOT-22, VAS and rated their symptoms according to MMS and impact on quality of life. RESULTS: The median SNOT 22 scores varied between the 3 MMS categories. The interquartile ranges for the respective MMS groups were: Mild 8-17, Moderate 22.5-48, Severe 54-83. Median values for the respective MMs groups were: Mild 12, Moderate 36 and Severe 66. 15.38% of patients in the Mild category, 95.24% in the Moderate category and 100% in the Severe category feel their QoL is affected. There was a strongly positive correlation between the SNOT-22 and VAS scores. CONCLUSION: We propose a statistically validated definition for stratification of the SNOT-22, with Mild being defined on the SNOT-22 score as 8-20 inclusive, Moderate as >20-50 and Severe as >50.

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.

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 score0.005
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.035
GPT teacher head0.292
Teacher spread0.257 · 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