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Record W4287527612 · doi:10.4103/kjo.kjo_194_21

Connect, collaborate, contribute, and create

2021· article· en· W4287527612 on OpenAlexaboutno aff
V Sudha

Bibliographic record

VenueKerala Journal of Ophthalmology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTeamworkPublic relationsPopularityWorkforceAction (physics)Health careWork (physics)PsychologyProduct (mathematics)Medical educationBusinessMedicinePolitical scienceEngineeringSocial psychology

Abstract

fetched live from OpenAlex

“No one is big enough to be independent of others.” -Will Mayo As this Editorial team reaches the end of its 2 year tenure, I wish to thank each and every member of the team for the excellent teamwork and cooperation I received during this time. Selecting articles, reviewing, writing up sections, proofreading, and encouraging others to contribute, were all done in a timely and effective manner. Hopefully, we have together been able to raise the standards of our Journal. The encouragement and effective suggestions I received from my senior colleagues, timely reviews by expert reviewers from KSOS and outside, played a very important role in keeping the momentum going. In my final editorial, I would like to highlight this extremely important concept of Teamwork and Collaboration. The concept of Networking for individual professional upliftment is gaining popularity. However, if we can cooperate with each other for the benefit of Ophthalmology as a whole, and patient treatment patterns, in particular, we reach nearer to our shared goal of reducing visual disability. EVERYBODY WINS The World Health Organization (WHO) recognizes that collaborative practice strengthens health systems and improves health outcomes, and is an innovative strategy that will play an important role in mitigating the global health workforce crisis. The Framework for Action on Interprofessional Education and Collaborative Practice is the product of the WHO Study Group on this practice.[1] Collaboration is evident when health-care professionals communicate with each other, assume complementary roles to cooperatively work together, and share responsibility for problem-solving and decision-making. Specific collaborative activities include sharing of information, discussion of complicated cases, and referrals to colleagues. The developing concept of Group Practice and its advantages has been highlighted by the President, KSOS, in the following pages of this journal issue. COLLABORATIONS IN OPHTHALMOLOGY Being able to take care of the sight of a patient is both a responsibility and a privilege. Because this is our ultimate goal: to eliminate blindness and visual impairement. Many useful examples of cooperation have been seen in Ophthalmology. Research Collaboration is vital in driving forward research and innovation seeking to develop solutions to refine the diagnosis, management, and treatment of patients with eye diseases. There is a well-identified need for patient-oriented clinical research and this can only be achieved by creating active collaboration between academic centers with competence for clinical research, with support by an infrastructure that provides appropriate management of clinical trials at a realistic cost. In Ophthalmology, the example was set by the Diabetic Retinopathy Clinical Research (DRCR. net) Retina Network in the USA, formed in 2002 through a National Eye Institute and National Institute of Diabetes and Digestive and Kidney Diseases-sponsored cooperative agreement. The objective was to develop a collaborative network to facilitate multicenter clinical research on Diabetic Retinopathy and Diabetic Macular Edema, and has now been expanded to include research on other retinal diseases. It used the combined strengths of academic and community retina sites in the infrastructure as well as created opportunities for industry collaboration while maintaining rigorous academic independence from pharmaceutical interests.[2] Since 2002, the DRCR. net has initiated and completed numerous multicenter studies in DR with over 160 participating sites and 500 physicians throughout the United States and Canada. Multicenter Data Retrieval Data from various sources can be integrated into a common registry and provide important information. Useful applications can be derived too from these. Data derived from the Sight Outcomes Research Collaborative Ophthalmology Data Repository, which captures electronic health record data of all patients receiving any eye care at academic medical centers, was used for developing an algorithm useful in triaging patients in the COVID pandemic based on glaucoma severity and progression risk by identifying patients whose appointments could safely get postponed and facilitated prioritization of appointments for rescheduling.[3] Clinical Practice Guidelines We are beginning to work together to develop consensus statements about eye care and train ourselves on how to use best practice guidelines. An international, expert-led consensus initiative was set up by the Collaborative Ocular Tuberculosis Study group to develop systematic, evidence, and experience-based recommendations for the treatment of ocular TB using a modified Delphi technique process.[4] Sharing Knowledge And Expertise Improving education and training to raise standards in Ophthalmology worldwide[56] is being followed by many important organizations, and is best exemplified by the online academic resource, EyeWiki, which is a collaboration between the American Academy of Ophthalmology and multiple societies.[7] Networking for Professional Career Advancement There is a critical need to help ophthalmologists maintain their competency and learn new skills, forging valuable relationships with peers. Organized mentorship programs can play a key role in fostering the development of careers in ophthalmology.[8] Stronger personal relationships can nurture innovative strategies with colleagues, engage new energy, and maintain momentum when obstacles seem overwhelming. Public Education Public–private partnerships can improve population health by advancing public health strategies and policies, improving public health education and advocacy, fostering trust and collaboration among sectors and stakeholders, and improving access to health care.[9] Newer Technologies A universal artificial intelligence (AI) platform developed for collaborative management of cataracts involving multilevel clinical scenarios explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. It showed robust diagnostic performance and effective service for cataracts.[10] Learning how to work in teams, brainstorming on issues, and networking with the right people can improve our individual practices. Publications based on multicentric research can provide data on comprehensive real-life effectiveness of various treatment strategies, especially in resource-poor regions where implementing strict guidelines may not be feasible. Innovations like using AI in Ophthalmology are made possible through collaboration among scientists, medical professionals, and technological experts. Thus, this concept needs to be nurtured and encouraged by all professional societies. Clarity and transparency in the collaboratorship process are essential in nurturing these networks and carrying them forward in future practice. Signing off with best wishes to the incoming Editorial team ……

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.445
Teacher spread0.389 · 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.

Study designNot applicable
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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