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Record W2210170634

脳卒中における新しい画像診断・治療補助システムの開発と構築〜携帯端末(iPhone)を用いた早期診断・治療を目指して〜

2010· article· ja· W2210170634 on OpenAlexaboutno aff
高尾洋之

Bibliographic record

Venue脳神経外科速報 · 2010
Typearticle
Languageja
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

急性期脳梗塞に対する遺伝子組み換え組織プラスミノゲンアクチベーター(rt-PA,商品名:アルテプラーゼ.)の有効性が欧米の臨床治験により証明され,わが国でも平成17(2005)年10 月11 日より,発症3 時間以内の脳梗塞患者に使用可能となった.Canadian Stroke Registry3)によれば,搬送の5 分の遅れは,rt-PA 静脈投与できる確率を2%減少させ,病院内での治療が5 分遅れれば,効果不良となる確率が5%増加するという.すなわち,rt-PA 静脈投与の施行には,適応基準の厳守とともに可能な限りの迅速な対応(欧米では,適応が3 時間以内から4 時間30 分以内に延びた)が必要であると言えよう1,2,4). また近年,急性期脳梗塞に対する血管内治療による血栓除去術(mechanical thrombectomy)は諸外国から良好な治療成績が報告されており5 〜 8),わが国でも近々認可される予定である. しかしながら,急性期脳卒中治療を有効に行ううえで解決すべき問題も少なくない.現在,国内の中小規模の病院の多くは,経験豊富なベテラン医師,専門医の24 時間体制の勤務形態をとることは難しい.そのため,患者の受け入れが不可能になることや診断・治療適応を含め,専門医師がいないために診断や治療のタイミングが遅れて治療ができないこともある. われわれは,脳卒中の患者の受け入れから専門医へのコンサルトをより有効かつ効果的に行うため,現在広く普及している携帯電話(iPhone)を利用した遠隔画像診断・治療補助システムを開発したので報告する.

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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.004
Scholarly communication0.0060.005
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0230.010

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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