Evidence map›Paper›PMID 42630259›Full record

ArticleArthroscopy, sports medicine, and rehabilitation2026

Lateral Extra-articular Tenodesis Internet Search Trends Correlate to the Quantity of Publications From 2020 to 2024.

Mia Lustig, Sean Hazzard, Mark Cote, Danielle Lonati, Peter Asnis

Abstract read
In one paragraph

Article in Arthroscopy, sports medicine, and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mia LustigMassachusetts General Hospital Waltham Massachusetts U.S.A.ORCID https://orcid.org/0009-0003-8559-2841
Sean HazzardMassachusetts General Hospital Waltham Massachusetts U.S.A.ORCID https://orcid.org/0000-0001-7186-1199
Mark CoteMassachusetts General Hospital Waltham Massachusetts U.S.A.ORCID https://orcid.org/0000-0001-5805-9273
Danielle LonatiFrank Netter School of Medicine at Quinnipiac North Haven Connecticut U.S.A.ORCID https://orcid.org/0000-0002-3167-5946
Peter AsnisMassachusetts General Hospital Waltham Massachusetts U.S.A.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To investigate public internet search trends for lateral extra-articular tenodesis (LET) and examine their potential relation to the peer-reviewed literature on LET between 2020 and 2024. Methods: Search term data from February 2020 to December 2024 was collected via the Google Trends feature. Worldwide weekly relative search volumes (RSVs) were populated based on the key terms: "lateral extra-articular tenodesis" and "knee LET." Concurrently, a literature search of the PubMed database was performed to quantify the volume of published literature containing identical phrases in the title within the same timeframe. General linear regression was used to model the relation between RSV and the published literature over time. Results: During the study timeframe, the total publications increased from 21 to 45. The public's RSV regarding LET procedures increased exponentially from March 2020 to December 2024. The relation between the RSV and the cumulative publications in this time period shows an adjusted Conclusions: Both the public interest in LET and the number of publications increased rapidly between 2020 and 2024. Clinical Relevance: The increasing public interest in LET, which parallels the growth in the peer-reviewed literature, highlights the need for clinicians to remain informed about evolving evidence, as patients may seek guidance and request LET based on emerging data and online information.

Identifiers

PMID42630259
PMCPMC13493324

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.