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Record W128013518

Cost analysis of intra-articular sodium hyaluronate treatment in knee osteoarthritis patients who failed conservative treatment.

2007· article· en· W128013518 on OpenAlexaboutno aff
Thana Turajane, Viroj Labpiboonpong, Samart Maungsiri

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisWOMACSurgeryConservative treatmentSodium hyaluronateRetrospective cohort studyTotal knee arthroplastyArthroplasty
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Failed conservative treatments of knee osteoarthritis (OA) in the elderly have traditionally been treated with TKA (Total Knee Arthroplasty). Although TKA is a gold standard and cost-effective treatment in elderly patients, it should be considered as the last resource for patients with pain that cannot be controlled by the usual conservative therapeutic approaches. Numerous studies showed that intra-articular Sodium Hyaluronate (IA-HA) (Hyalgan) is effective for treatment in various stages of knee OA. OBJECTIVES: To compare cost of treatment between two groups of knee OA patients who failed conservative treatments. The first group includes the patients who responded to IA-HA treatment leading to delay or cancel surgical treatments (response group). The second group includes the patients who did not respond to IA-HA treatment and they had to undergo surgical procedures (non-response group). MATERIAL AND METHOD: A cost analysis from the retrospective data in Police General Hospital from year 2001-2004. One hundred and eighty three patients with knee OA (208 knees) who failed conservative treatments and did not have contraindications for surgery were enrolled. All patients were treated with one course of three IA-HA injections (500-730 KDA, Hyalgan) at weekly intervals and followed up for a minimum 2-year period. In case of successful treatment (response group), repeated doses were recommended. If the patients did not improve in the average Western Ontario and McMaster Universities Osteoarthritis Index (the average WOMAC) score within one month after completion of the injections, they would be classified as a non-response group and the surgical procedures would be considered. Cost of direct medical costs (drugs), hospitalization, and resource utilization were recorded and analyzed. RESULTS: One hundred and forty six patients (164 knees) responded to the treatment and did not need any surgical procedures within the 2-year follow-up period. Thirty-seven patients (44 knees) did not respond and needed surgical procedures. In the response group, 83 patients repeated the second course of treatment and 14 patients repeated the third course. The total average cost for the response group were 47,044.18 Baht per patient, which was an average cost of IA-HA; 12,240.41 Baht and an average cost of other medications following the injection of 34, 803.77 Baht. The ratio of the IA-HA cost and medications following the injection cost was 1:2.84. In the non-response group, the total average cost was 144,884 Baht per patient including average cost of surgery of 135,559.95 Baht per patient or 113,993.59 Baht per knee and cost of IA-HA treatment of 9,324 Baht per patient, which was only 6.44% of the total costs of treatment. However, when considered in the response group, the IA-HA treatment provided cost saving from cancellation or delayed surgical procedures at 63.26%. CONCLUSION: IA-HA should be considered as a medical intervention before surgical procedures in knee OA patients who failed conservative treatments. Even though the cost of IA-HA treatment would increase the total costs of treatment and some patients might fail, it was only 6.44% of the total costs. On the other hand, if patients responded to IA-HA treatment, then the surgical procedures were not required. This treatment could save the cost from cancellation or delayed surgical procedures at 63.26%.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.249
Teacher spread0.226 · 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 designObservational
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".

Quick stats

Citations128
Published2007
Admission routes1
Has abstractyes

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