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9 merged results for "Cresco, Ltd. (Japan)"

Partial results: at least one source did not answer. Available results are shown rather than treating an upstream outage as zero matches.

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  1. Use of explainable AI on slit-lamp images of anterior surface of eyes to diagnose allergic conjunctival diseases.

    Yonehara M, Nakagawa Y, Ayatsuka Y, Hara Y · 2025 · Allergology international : official journal of the Japanese Society of Allergology

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.1016/j.alit.2024.07.004

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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  2. Using artificial intelligence to detect keratoconus progression based on age and anterior segment optical coherence tomography images.

    Miyai T, Kamiya K, Ayatsuka Y, Kato Y · 2026 · Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.1016/j.apjo.2026.100323

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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  3. A global-scale screening of non-native aquatic organisms to identify potentially invasive species under current and future climate conditions.

    Vilizzi L, Copp GH, Hill JE, Adamovich B · 2021 · The Science of the total environment

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.1016/j.scitotenv.2021.147868

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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  4. Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.

    Koyama A, Miyazaki D, Nakagawa Y, Ayatsuka Y · 2021 · Scientific reports

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.1038/s41598-021-02138-w

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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  5. Multimodal artificial intelligence for retinal detachment diagnosis using fundus imaging and patient questionnaires.

    Yonemaru N, Tabuchi H, Deguchi H, Omi Y · 2026 · The British journal of ophthalmology

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.1136/bjo-2025-327506

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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  6. Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study.

    Kamiya K, Ayatsuka Y, Kato Y, Fujimura F · 2019 · BMJ open

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.1136/bmjopen-2019-031313

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  7. Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images.

    Kuwayama S, Ayatsuka Y, Yanagisono D, Uta T · 2019 · Journal of ophthalmology

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.1155/2019/6319581

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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  8. Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps.

    Kamiya K, Ayatsuka Y, Kato Y, Shoji N · 2021 · Annals of translational medicine

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.21037/atm-21-1772

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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  9. Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography.

    Kamiya K, Ayatsuka Y, Kato Y, Shoji N · 2021 · Frontiers in medicine

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.3389/fmed.2021.724902

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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer